# Best Data Science and Machine Learning Platforms *By [Bijou Barry](https://research.g2.com/insights/author/bijou-barry)* Data science and machine learning (DSML) platforms provide tools to build, deploy, and monitor machine learning (ML) algorithms by combining data with intelligent, decision-making models to support business solutions. These platforms may offer prebuilt algorithms and visual workflows for nontechnical users or require more advanced development skills for complex model creation. Core capabilities of data science and machine learning (DSML) software To qualify for inclusion in the Data Science and Machine Learning (DSML) Platforms category, a product must: - Present a way for developers to connect data to algorithms so they can learn and adapt - Allow users to create ML algorithms and offer prebuilt algorithms for novice users - Provide a platform for deploying AI at scale How DSML software differs from other tools DSML platforms differ from traditional platform-as-a-service (PaaS) offerings by providing ML–specific functionality, such as prebuilt algorithms, model training workflows, and automated features that reduce the need for extensive data science expertise. Insights from G2 Reviews on DSML software According to G2 review data, users highlight the value of streamlined model development, ease of deployment, and options that support both nontechnical and advanced practitioners through visual interfaces or coding-based workflows. ## Top Data Science and Machine Learning Platforms at a Glance | # | Product | Rating | Best For | What Users Say | |---|---------|--------|----------|----------------| | 1 | [Databricks](https://www.g2.com/products/databricks/reviews) | 4.6/5.0 (1,316 reviews) | Unified lakehouse ML and analytics workflows | "[Premium Notebook Experience That Unifies ML and Data Engineering](https://www.g2.com/survey_responses/databricks-review-13086971)" | | 2 | [Gemini Enterprise Agent Platform](https://www.g2.com/products/gemini-enterprise-agent-platform/reviews) | 4.3/5.0 (653 reviews) | End-to-end ML lifecycle with GCP-native MLOps | "[Vertex AI Streamlines ML Training and Deployment with a Unified, Feature-Rich Platform](https://www.g2.com/survey_responses/gemini-enterprise-agent-platform-review-12437893)" | | 3 | [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews) | 4.3/5.0 (764 reviews) | End-to-end ML lifecycle with governed model deployment | "[Valuable, Data-Driven Insights That Keep Getting Better](https://www.g2.com/survey_responses/sas-viya-review-13100729)" | | 4 | [Snowflake](https://www.g2.com/products/snowflake/reviews) | 4.5/5.0 (707 reviews) | SQL-native ML pipelines with unified data warehousing | "[Easy, Efficient Data Extraction with Clear Database Insights](https://www.g2.com/survey_responses/snowflake-review-12884116)" | | 5 | [Dataiku](https://www.g2.com/products/dataiku/reviews) | 4.4/5.0 (210 reviews) | End-to-end ML workflows with no-code/code flexibility | "[Dataiku: No-Code ETL Powerhouse — Collaborative, Visual, and Python/SQL Friendly](https://www.g2.com/survey_responses/dataiku-review-13046146)" | | 6 | [MATLAB](https://www.g2.com/products/matlab/reviews) | 4.5/5.0 (750 reviews) | Numerical simulation and ML algorithm prototyping | "[A Robust Powerhouse for Advanced Engineering Simulations and Modeling](https://www.g2.com/survey_responses/matlab-review-12689149)" | | 7 | [Hex](https://www.g2.com/products/hex-tech-hex/reviews) | 4.5/5.0 (401 reviews) | Polyglot SQL-Python notebooks with AI-assisted analysis | "[Effortless Data Analysis with Powerful AI](https://www.g2.com/survey_responses/hex-review-12262172)" | | 8 | [Deepnote](https://www.g2.com/products/deepnote/reviews) | 4.5/5.0 (378 reviews) | Collaborative notebook analytics with multi-source integration | "[Clarity for complex nutrition work](https://www.g2.com/survey_responses/deepnote-review-12699174)" | | 9 | [IBM watsonx.data](https://www.g2.com/products/ibm-watsonx-data/reviews) | 4.4/5.0 (159 reviews) | Unified lakehouse analytics for hybrid AI workloads | "[Powerful Query Performance and Governance, But a Steep Onboarding Learning Curve](https://www.g2.com/survey_responses/ibm-watsonx-data-review-12836202)" | | 10 | [Anaconda Core](https://www.g2.com/products/anaconda-core/reviews) | 4.5/5.0 (235 reviews) | Dependency-free Python environment setup for data science | "[All-in-One Toolkit for Data Science Workflows](https://www.g2.com/survey_responses/anaconda-core-review-12706297)" | ## G2 Grid® for Data Science and Machine Learning Platforms ![G2 Grid® for Data Science and Machine Learning Platforms plotting products by satisfaction and market presence](https://www.g2.com/categories/data-science-and-machine-learning-platforms/grids.png?focus%5B%5D=10470&focus%5B%5D=21469&focus%5B%5D=1327283&focus%5B%5D=10938&focus%5B%5D=24457&focus%5B%5D=1308796&focus%5B%5D=162504&focus%5B%5D=142374) Highlighted products: Databricks, Gemini Enterprise Agent Platform, SAS Viya, Snowflake, MATLAB, IBM watsonx.data, Hex, and Deepnote. Underlying data: [Grid® JSON](https://www.g2.com/categories/data-science-and-machine-learning-platforms/grids.json?focus%5B%5D=databricks&focus%5B%5D=gemini-enterprise-agent-platform&focus%5B%5D=sas-sas-viya&focus%5B%5D=snowflake&focus%5B%5D=matlab&focus%5B%5D=ibm-watsonx-data&focus%5B%5D=hex-tech-hex&focus%5B%5D=deepnote) ## How Many Data Science and Machine Learning Platforms Products Does G2 Track? **Total Products under this Category:** 966 ### Category Stats (Jul 2026) - **Average Rating**: 4.45/5 The average rating of products in this category, based on all submitted ratings - **Top Trending Product**: SutraAI (+14.29%) - Among all products in this category, SutraAI recorded the largest rating increase compared to last month *Last updated: July 13, 2026* ## How Does G2 Rank Data Science and Machine Learning Platforms Products? **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 13,900+ Authentic Reviews - 966+ Products - Unbiased Rankings G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category. ## Which Data Science and Machine Learning Platforms Is Best for Your Use Case? - **Leader:** [Databricks](https://www.g2.com/products/databricks/reviews) - **Highest Performer:** [Saturn Cloud](https://www.g2.com/products/saturn-cloud-saturn-cloud/reviews) - **Easiest to Use:** [Databricks](https://www.g2.com/products/databricks/reviews) - **Top Trending:** [Hex](https://www.g2.com/products/hex-tech-hex/reviews) - **Best Free Software:** [Databricks](https://www.g2.com/products/databricks/reviews) --- **Sponsored** ### Cloudera Cloudera is the only hybrid data and AI platform company that large organizations trust to bring AI to their data anywhere it lives. Unlike other providers, Cloudera delivers a consistent cloud experience that converges public clouds, on-prem data centers, and the edge, leveraging a proven open-source foundation. As the pioneer in big data, Cloudera empowers businesses to apply AI and assert control over 100% of their data, in all forms, improving security, governance, and real-time and predictive insights. The world’s largest brands across all industries rely on Cloudera to transform decision-making and ultimately boost bottom lines, safeguard against threats, and save lives. The Cloudera data and AI platform includes: Cloudera AI: Deploy and scale any AI model, anywhere. Cloudera brings compute to governed data where it lives for Private AI anywhere by design. Complete control, security, and governance of mission-critical data, models, agents, and inference ensure faster sovereign AI deployments. Cloudera Data-in-Motion: Make fast decisions from real-time data anywhere. Move data with any structure from any source to any destination seamlessly across hybrid environments, enabling in-the-moment business-critical decisions by processing and analyzing real-time data anywhere, from the edge to AI, as business happens. Cloudera Open Data Lakehouse: Process any data, anywhere, for actionable insights. Make smart decisions with an open data lakehouse powered by Apache Iceberg that delivers trusted, reliable, and unified data to fuel agents, AI applications, and analytics, improving collaboration, breaking silos, and simplifying sharing. Cloudera Unified Data Fabric: Unify security and governance across the entire data estate. Move beyond fragmented data management: Break down silos and connect disparate data sources intelligently and securely to provide a unified view of all organizational data and centralized end-to-end control across complex hybrid data environments. [Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list&secure%5Bcategory_id%5D=692&secure%5Bchosen_at%5D=2026-07-14T04%3A41%3A08Z&secure%5Bdisplayable_resource_id%5D=692&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=692&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=1886&secure%5Bresource_id%5D=692&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fdata-science-and-machine-learning-platforms&secure%5Btoken%5D=c0dd3ae82aaf708cf5ad1deed7d124f2a77cda4523f492be978fa8b4d3691627&secure%5Burl%5D=https%3A%2F%2Fwww.cloudera.com%2Fproducts%2Fcloudera-data-platform%2Fcdp-demos.html%3Finternal_link%3Dp18%23get-started&secure%5Burl_type%5D=custom_url) --- ## What Are the Top-Rated Data Science and Machine Learning Platforms Products in 2026? ### 1. [Databricks](https://www.g2.com/products/databricks/reviews) Databricks is a unified data and AI platform that helps organizations build, govern and scale data pipelines, analytics, machine learning, AI applications and agents. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on Databricks to work with enterprise data and AI at scale. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Agent Bricks, Lakeflow, Lakehouse, Lakebase, Genie and Unity Catalog. Founded in 2013 by the original creators of Apache Spark™, Delta Lake, MLflow and Unity Catalog, Databricks is built on an open lakehouse architecture that brings data, analytics and AI together. The platform is used by data engineers, data scientists, analysts, developers, machine learning teams, AI teams and business users to collaborate across the full data and AI lifecycle. Key Databricks capabilities include: - Data engineering: Build, automate and manage reliable batch, streaming and real-time data pipelines. - Analytics and business intelligence: Run SQL analytics, create dashboards and enable business teams to explore data. - Data governance: Discover, secure and manage data and AI assets across teams, clouds and workloads. - Machine learning and AI: Develop models, build generative AI applications and create production-grade AI agents. - Data applications: Build and deploy data-driven applications using governed enterprise data. Available across AWS, Azure and Google Cloud, Databricks helps organizations work across clouds, reduce data silos and simplify collaboration across teams and tools. Customers use Databricks for use cases such as customer personalization, fraud detection, predictive maintenance, real-time analytics, cybersecurity, healthcare research, financial risk management, supply chain optimization and AI-powered decision-making. Databricks is used across industries including financial services, healthcare and life sciences, retail, manufacturing, energy and the public sector. Organizations use the platform to modernize data infrastructure, accelerate AI adoption and turn enterprise data into business value. **Average Rating:** 4.6/5.0 **Total Reviews:** 1,316 **How Do G2 Users Rate Databricks?** - **Application:** 8.7/10 (Category avg: 8.5/10) - **Managed Service:** 8.5/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.4/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.4/10 (Category avg: 8.5/10) **Who Is the Company Behind Databricks?** - **Seller:** [Databricks Inc.](https://www.g2.com/sellers/databricks-inc) - **Company Website:** https://databricks.com - **Year Founded:** 2013 - **HQ Location:** San Francisco, CA - **Twitter:** @databricks (92,269 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/3477522/ (15,627 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Engineer, Data Analyst - **Top Industries:** Information Technology and Services, Financial Services - **Company Size:** 48% Enterprise, 38% Mid-Market #### What Are Databricks's Pros and Cons? **Pros:** - Features (192 reviews) - Ease of Use (155 reviews) - Integrations (141 reviews) - Collaboration (114 reviews) - Analytics (113 reviews) **Cons:** - Learning Curve (78 reviews) - Expensive (71 reviews) - Steep Learning Curve (64 reviews) - Complexity (45 reviews) - Complex Setup (35 reviews) ### What Do G2 Reviewers Say About Databricks? *AI-generated summary from verified user reviews* **Pros:** - Users value the **ease of use and comprehensive features** of Databricks, enhancing their data processing and machine learning tasks. - Users commend the **ease of use** of Databricks, enhancing their experience with efficient data management and integration. - Users value the **seamless integrations** of Databricks with AWS and various tools, enhancing their organizational capabilities. - Users value the **seamless collaboration** capabilities of Databricks, enhancing teamwork on data projects and insights sharing. - Users appreciate the **wide array of integrated analytical features** in Databricks, enhancing collaborative data insights and performance. **Cons:** - Users face a steep **learning curve** with Databricks due to its complex permissions and compute modes. - Users find the **costs of Databricks to be high** , especially when working with large datasets. - Users find the **steep learning curve** of Databricks challenging, particularly for newcomers to big data tools. - Users find the **complexity** of Databricks challenging, particularly during initial setup and with advanced features. - Users find the **complex setup** of Databricks challenging initially, but support helps in overcoming hurdles quickly. #### What Are Recent G2 Reviews of Databricks? **"[Helpful for Managing and Analyzing Operational Data](https://www.g2.com/survey_responses/databricks-review-13090803)"** **Rating:** 4.5/5.0 stars *— Vishaka C.* [Read full review](https://www.g2.com/survey_responses/databricks-review-13090803) --- **"[Premium Notebook Experience That Unifies ML and Data Engineering](https://www.g2.com/survey_responses/databricks-review-13086971)"** **Rating:** 5.0/5.0 stars *— Rachit S.* [Read full review](https://www.g2.com/survey_responses/databricks-review-13086971) --- #### What Are G2 Users Discussing About Databricks? - [What does Databricks software do?](https://www.g2.com/discussions/what-does-databricks-software-do) - 3 comments - [What is Databricks unified analytics platform?](https://www.g2.com/discussions/what-is-databricks-unified-analytics-platform) - 3 comments - [What is Lakehouse in Databricks?](https://www.g2.com/discussions/what-is-lakehouse-in-databricks) - 4 comments, 2 upvotes - [What are the features of Databricks?](https://www.g2.com/discussions/what-are-the-features-of-databricks) - 4 comments, 2 upvotes ### 2. [Gemini Enterprise Agent Platform](https://www.g2.com/products/gemini-enterprise-agent-platform/reviews) Build, deploy, and scale machine learning (ML) models faster, with fully managed ML tools for any use case. Through Vertex AI Workbench, Vertex AI is natively integrated with BigQuery, Dataproc, and Spark. You can use BigQuery ML to create and execute machine learning models in BigQuery using standard SQL queries on existing business intelligence tools and spreadsheets, or you can export datasets from BigQuery directly into Vertex AI Workbench and run your models from there. Use Vertex Data Labeling to generate highly accurate labels for your data collection. **Average Rating:** 4.3/5.0 **Total Reviews:** 653 **How Do G2 Users Rate Gemini Enterprise Agent Platform?** - **Application:** 8.3/10 (Category avg: 8.5/10) - **Managed Service:** 8.3/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.5/10 (Category avg: 8.2/10) - **Ease of Admin:** 7.9/10 (Category avg: 8.5/10) **Who Is the Company Behind Gemini Enterprise Agent Platform?** - **Seller:** [Google](https://www.g2.com/sellers/google) - **Year Founded:** 1998 - **HQ Location:** Mountain View, CA - **Twitter:** @google (31,899,995 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (341,888 employees on LinkedIn®) - **Ownership:** NASDAQ:GOOG **Who Uses This Product?** - **Who Uses This:** Software Engineer, Data Scientist - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 42% Small-Business, 31% Enterprise #### What Are Gemini Enterprise Agent Platform's Pros and Cons? **Pros:** - Ease of Use (108 reviews) - Features (77 reviews) - Machine Learning (76 reviews) - Model Variety (69 reviews) - Integrated Platform (66 reviews) **Cons:** - Expensive (58 reviews) - Complexity (48 reviews) - Learning Curve (48 reviews) - Complexity Issues (43 reviews) - Difficult Learning (42 reviews) ### What Do G2 Reviewers Say About Gemini Enterprise Agent Platform? *AI-generated summary from verified user reviews* **Pros:** - Users find the **ease of use** of the Gemini Enterprise Agent Platform enhances their productivity and workflow significantly. - Users appreciate the **multimodal capabilities** and unified workflow of Gemini, enhancing productivity in machine learning tasks. - Users value the **multimodal capabilities** of Gemini for enhancing productivity in software development and automation. - Users appreciate the **multimodal capabilities** of Gemini, enhancing productivity in software development and automation projects. - Users appreciate the **integrated platform** of Gemini, enhancing productivity by unifying workflows for machine learning and automation. **Cons:** - Users find the **pricing transparency challenging** , as costs can escalate quickly if not managed carefully. - Users find the **complexity** of Vertex AI daunting, particularly with project setup and advanced feature navigation. - Users find the **learning curve steep** , particularly for newcomers unfamiliar with Google Cloud's organization and ML Ops. - Users struggle with the **complexity issues** of Gemini Enterprise Agent Platform, finding it costly and difficult to learn. - Users find the **learning curve challenging** , especially with unstructured data and pricing complexity affecting their experience. #### What Are Recent G2 Reviews of Gemini Enterprise Agent Platform? **"[Vertex AI Streamlines ML Training and Deployment with a Unified, Feature-Rich Platform](https://www.g2.com/survey_responses/gemini-enterprise-agent-platform-review-12437893)"** **Rating:** 5.0/5.0 stars *— Danyal A.* [Read full review](https://www.g2.com/survey_responses/gemini-enterprise-agent-platform-review-12437893) --- **"[Seamless Google Suite Integration for Everyday Work](https://www.g2.com/survey_responses/gemini-enterprise-agent-platform-review-12855480)"** **Rating:** 4.5/5.0 stars *— Shubham S.* [Read full review](https://www.g2.com/survey_responses/gemini-enterprise-agent-platform-review-12855480) --- #### What Are G2 Users Discussing About Gemini Enterprise Agent Platform? - [What is Google Cloud AI Platform used for?](https://www.g2.com/discussions/what-is-google-cloud-ai-platform-used-for) - 3 comments, 4 upvotes - [What software libraries does cloud ML engine support?](https://www.g2.com/discussions/what-software-libraries-does-cloud-ml-engine-support) - 3 comments, 4 upvotes - [How do I use Google cloud platform for machine learning?](https://www.g2.com/discussions/how-do-i-use-google-cloud-platform-for-machine-learning) - [Is Google Cloud AI free?](https://www.g2.com/discussions/is-google-cloud-ai-free) - [What is Google AI platform?](https://www.g2.com/discussions/what-is-google-ai-platform) - 2 comments, 2 upvotes ### 3. [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews) SAS Viya is a cloud-native data and AI platform that enables teams to build, deploy and scale explainable AI that drives trusted, confident decisions. It unites the entire data and AI life cycle and empowers teams to innovate quickly while balancing speed, automation and governance by design. Viya unifies data management, advanced analytics and decisioning in a single platform, so organizations can move from experimentation to production with confidence, delivering measurable business impact that is secure, explainable and scalable across any environment. Key capabilities required to deliver trusted decisions include: • End-to-end clarity across the data and AI life cycle, with built-in lineage, auditability and continuous monitoring to support defensible decisions. • Governance by design, enabling consistent oversight across data, models and decisions to reduce risk and accelerate adoption. • Explainable AI at scale, so insights and outcomes can be understood, validated and trusted by business and regulators alike. • Operationalized analytics, ensuring value continues beyond deployment through monitoring, retraining and life cycle management. • Flexible, cloud-native deployment, allowing organizations to start anywhere and scale everywhere while maintaining control. **Average Rating:** 4.3/5.0 **Total Reviews:** 764 **How Do G2 Users Rate SAS Viya?** - **Application:** 7.8/10 (Category avg: 8.5/10) - **Managed Service:** 7.9/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 7.8/10 (Category avg: 8.2/10) - **Ease of Admin:** 7.6/10 (Category avg: 8.5/10) **Who Is the Company Behind SAS Viya?** - **Seller:** [SAS Institute Inc.](https://www.g2.com/sellers/sas-institute-inc-df6dde22-a5e5-4913-8b21-4fa0c6c5c7c2) - **Company Website:** https://www.sas.com/ - **Year Founded:** 1976 - **HQ Location:** Cary, NC - **Twitter:** @SASsoftware (60,863 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1491/ (18,638 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Student, Statistical Programmer - **Top Industries:** Pharmaceuticals, Banking - **Company Size:** 33% Small-Business, 33% Enterprise #### What Are SAS Viya's Pros and Cons? **Pros:** - Ease of Use (316 reviews) - Features (218 reviews) - Analytics (196 reviews) - Data Analysis (166 reviews) - User Interface (147 reviews) **Cons:** - Learning Difficulty (151 reviews) - Learning Curve (144 reviews) - Complexity (143 reviews) - Difficult Learning (117 reviews) - Expensive (108 reviews) ### What Do G2 Reviewers Say About SAS Viya? *AI-generated summary from verified user reviews* **Pros:** - Users value the **user-friendly interface** of SAS Viya, which simplifies data analysis for all proficiency levels. - Users praise SAS Viya for its **advanced analytical capabilities** , enabling real-time insights and decision-making across various industries. - Users value the **advanced analytical capabilities** of SAS Viya, enhancing decision-making and providing deep business insights. - Users value the **end-to-end data lifecycle tooling** of SAS Viya, enhancing insights and decision-making across our organization. - Users appreciate the **user-friendly interface** of SAS Viya, enabling easy access for varied technical skills. **Cons:** - Users find SAS Viya **difficult to use for non-technical users** , impacting their ability to access reports and dashboards. - Users find the **learning curve steep** , making it challenging for non-technical users to utilize SAS Viya effectively. - Users find the **visualization complexity** of SAS Viya challenging, especially for non-technical and new users. - Users find the **difficult learning curve** challenging, especially for non-technical users accessing reports and dashboards. - Users find the **pricing to be expensive** and often unclear, making it a significant concern during assessment. #### What Are Recent G2 Reviews of SAS Viya? **"[Intuitive Interface with Fast, Practical Reporting for Massive Data](https://www.g2.com/survey_responses/sas-viya-review-13091171)"** **Rating:** 5.0/5.0 stars *— Nahom K.* [Read full review](https://www.g2.com/survey_responses/sas-viya-review-13091171) --- **"[Valuable, Data-Driven Insights That Keep Getting Better](https://www.g2.com/survey_responses/sas-viya-review-13100729)"** **Rating:** 5.0/5.0 stars *— Gilbert B.* [Read full review](https://www.g2.com/survey_responses/sas-viya-review-13100729) --- #### What Are G2 Users Discussing About SAS Viya? - [What is SAS Visual Data Mining and Machine Learning used for?](https://www.g2.com/discussions/what-is-sas-visual-data-mining-and-machine-learning-used-for) - 2 comments ### 4. [Snowflake](https://www.g2.com/products/snowflake/reviews) Snowflake makes enterprise AI easy, efficient and trusted. Thousands of companies around the globe, including hundreds of the world’s largest, use Snowflake’s AI Data Cloud to share data, build applications, and power their business with AI. The era of enterprise AI is here. Learn more at snowflake.com (NYSE: SNOW). **Average Rating:** 4.5/5.0 **Total Reviews:** 707 **How Do G2 Users Rate Snowflake?** - **Application:** 9.2/10 (Category avg: 8.5/10) - **Managed Service:** 9.0/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.6/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.7/10 (Category avg: 8.5/10) **Who Is the Company Behind Snowflake?** - **Seller:** [Snowflake, Inc.](https://www.g2.com/sellers/snowflake-inc) - **Company Website:** https://www.snowflake.com - **Year Founded:** 2012 - **HQ Location:** San Mateo, CA - **Twitter:** @SnowflakeDB (278 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/snowflake-computing/ (11,308 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Engineer, Data Analyst - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 45% Mid-Market, 43% Enterprise #### What Are Snowflake's Pros and Cons? **Pros:** - Ease of Use (183 reviews) - Features (118 reviews) - Data Management (108 reviews) - Scalability (99 reviews) - Performance (90 reviews) **Cons:** - Expensive (91 reviews) - Feature Limitations (54 reviews) - Learning Curve (45 reviews) - Cost (44 reviews) - Cost Management (44 reviews) ### What Do G2 Reviewers Say About Snowflake? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **ease of use** of Snowflake, making data sharing and analytics fast and straightforward. - Users enjoy the **reliable features and user-friendly interface** of Snowflake, enhancing their data analytics experience. - Users value the **easy-to-use data management** features of Snowflake, enhancing efficiency in warehousing projects. - Users value the **seamless scalability** of Snowflake, enabling smooth handling of complex queries and varying workloads effortlessly. - Users value the **fast data analysis and processing** capabilities of Snowflake, enhancing productivity without infrastructure concerns. **Cons:** - Users note that Snowflake can be **expensive** , especially for small businesses or those on a tight budget. - Users find **feature limitations** in Snowflake, such as inability to run multiple SQL statements efficiently and restrictive permissions. - Users find the **learning curve steep** for Snowflake, requiring training for inexperienced team members to adjust. - Users note that **cost control is challenging** with Snowflake; without discipline, expenses can escalate unexpectedly. - Users find **cost management challenging** with Snowflake, reporting higher initial costs and complexity in optimizing expenses. #### What Are Recent G2 Reviews of Snowflake? **"[Easy, Efficient Data Extraction with Clear Database Insights](https://www.g2.com/survey_responses/snowflake-review-12884116)"** **Rating:** 4.5/5.0 stars *— Pankaj K.* [Read full review](https://www.g2.com/survey_responses/snowflake-review-12884116) --- **"[Snowflake Simplifies Data Management at Scale](https://www.g2.com/survey_responses/snowflake-review-12898129)"** **Rating:** 4.0/5.0 stars *— Harshil A.* [Read full review](https://www.g2.com/survey_responses/snowflake-review-12898129) --- #### What Are G2 Users Discussing About Snowflake? - [What is Snowflake used for?](https://www.g2.com/discussions/what-is-snowflake-used-for) - 2 comments, 1 upvote ### 5. [Dataiku](https://www.g2.com/products/dataiku/reviews) Dataiku is the Platform for AI Success: the AI orchestration layer where enterprises build, deploy, and govern analytics, models, and agents at scale. It sits on top of the data platforms, clouds, and AI services you already use, working across all of them without locking you into any one. Dataiku expands who can build production AI, putting the right tools in the hands of data scientists and domain experts alike, from fraud analysts to demand planners. It orchestrates machine learning, rules, LLMs, and agents as one governed system, built on more than a decade of running production AI. Governance is part of the build rather than something bolted on afterward, so teams ship faster while keeping performance, cost, and risk under control. The result: AI that moves from experimentation to trusted, measurable execution now, not in 18 months. **Average Rating:** 4.4/5.0 **Total Reviews:** 210 **How Do G2 Users Rate Dataiku?** - **Application:** 8.3/10 (Category avg: 8.5/10) - **Managed Service:** 8.2/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 7.8/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.0/10 (Category avg: 8.5/10) **Who Is the Company Behind Dataiku?** - **Seller:** [Dataiku](https://www.g2.com/sellers/dataiku) - **Company Website:** https://Dataiku.com - **Year Founded:** 2013 - **HQ Location:** New York, NY - **Twitter:** @dataiku (22,917 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/dataiku/ (1,619 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Scientist, Data Analyst - **Top Industries:** Financial Services, Pharmaceuticals - **Company Size:** 60% Enterprise, 23% Mid-Market #### What Are Dataiku's Pros and Cons? **Pros:** - Features (80 reviews) - Ease of Use (79 reviews) - Usability (45 reviews) - Easy Integrations (43 reviews) - Productivity Improvement (41 reviews) **Cons:** - Learning Curve (43 reviews) - Steep Learning Curve (25 reviews) - Difficult Learning (23 reviews) - Slow Performance (23 reviews) - Expensive (22 reviews) ### What Do G2 Reviewers Say About Dataiku? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate how **Dataiku simplifies ML development** , enabling quick model training and evaluation while accepting various data formats. - Users find Dataiku's **ease of use** invaluable, simplifying data integration and making machine learning development straightforward. - Users appreciate the **ease of use** in Dataiku, enabling collaboration between technical and non-technical users seamlessly. - Users value the **easy integrations** of Dataiku, facilitating collaboration and streamlining the entire data pipeline process. - Users benefit from the **significant productivity improvement** provided by Dataiku's visual recipes and collaborative environment. **Cons:** - Users find the **steep learning curve** challenging for beginners, making initial experience difficult. - Users find the **steep learning curve** challenging, especially when mastering advanced features as beginners. - Users find the **difficult learning** curve challenging, especially for beginners trying to utilize advanced features effectively. - Users report **slow performance** with Dataiku, particularly when managing large datasets and experiencing UI lag in projects. - Users find Dataiku to be **expensive** , particularly challenging for small companies and beginners due to high costs. #### What Are Recent G2 Reviews of Dataiku? **"[From idea to model in minutes: Dataiku accelerates the team's work](https://www.g2.com/survey_responses/dataiku-review-12967713)"** **Rating:** 4.5/5.0 stars *— Bill C.* [Read full review](https://www.g2.com/survey_responses/dataiku-review-12967713) --- **"[Dataiku: No-Code ETL Powerhouse — Collaborative, Visual, and Python/SQL Friendly](https://www.g2.com/survey_responses/dataiku-review-13046146)"** **Rating:** 4.5/5.0 stars *— Marco J.* [Read full review](https://www.g2.com/survey_responses/dataiku-review-13046146) --- #### What Are G2 Users Discussing About Dataiku? - [Is Dataiku an ETL tool?](https://www.g2.com/discussions/is-dataiku-an-etl-tool) - [Is Dataiku web based?](https://www.g2.com/discussions/is-dataiku-web-based) - [What is DSS Dataiku?](https://www.g2.com/discussions/what-is-dss-dataiku) - [What is Dataiku DSS used for?](https://www.g2.com/discussions/what-is-dataiku-dss-used-for) ### 6. [MATLAB](https://www.g2.com/products/matlab/reviews) MATLAB is a high-level programming and numeric computing environment widely utilized by engineers and scientists for data analysis, algorithm development, and system modeling. It offers a desktop environment optimized for iterative analysis and design processes, coupled with a programming language that directly expresses matrix and array mathematics. The Live Editor feature enables users to create scripts that integrate code, output, and formatted text within an executable notebook. Key Features and Functionality: - Data Analysis: Tools for exploring, modeling, and analyzing data. - Graphics: Functions for visualizing and exploring data through various plots and charts. - Programming: Capabilities to create scripts, functions, and classes for customized workflows. - App Building: Facilities to develop desktop and web applications. - External Language Interfaces: Integration with languages such as Python, C/C++, Fortran, and Java. - Hardware Connectivity: Support for connecting MATLAB to various hardware platforms. - Parallel Computing: Ability to perform large-scale computations and parallelize simulations using multicore desktops, GPUs, clusters, and cloud resources. - Deployment: Options to share MATLAB programs and deploy them to enterprise applications, embedded devices, and cloud environments. Primary Value and User Solutions: MATLAB streamlines complex mathematical computations and data analysis tasks, enabling users to develop algorithms and models efficiently. Its comprehensive toolboxes and interactive apps facilitate rapid prototyping and iterative design, reducing development time. The platform's scalability allows for seamless transition from research to production, supporting deployment on various systems without extensive code modifications. By integrating with multiple programming languages and hardware platforms, MATLAB provides a versatile environment that addresses the diverse needs of engineers and scientists across industries. **Average Rating:** 4.5/5.0 **Total Reviews:** 750 **How Do G2 Users Rate MATLAB?** - **Application:** 8.6/10 (Category avg: 8.5/10) - **Managed Service:** 8.3/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.5/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.4/10 (Category avg: 8.5/10) **Who Is the Company Behind MATLAB?** - **Seller:** [MathWorks](https://www.g2.com/sellers/mathworks) - **Year Founded:** 1984 - **HQ Location:** Natick, MA - **Twitter:** @MATLAB (105,142 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1194036/ (7,985 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Student, Graduate Research Assistant - **Top Industries:** Higher Education, Research - **Company Size:** 42% Enterprise, 31% Small-Business #### What Are MATLAB's Pros and Cons? **Pros:** - Ease of Use (19 reviews) - Features (16 reviews) - Data Visualization (13 reviews) - Tools Variety (10 reviews) - Simulation (9 reviews) **Cons:** - Expensive (12 reviews) - Slow Performance (10 reviews) - High System Requirements (7 reviews) - Expensive Licensing (4 reviews) - Lagging Performance (4 reviews) ### What Do G2 Reviewers Say About MATLAB? *AI-generated summary from verified user reviews* **Pros:** - Users find MATLAB's **ease of use** invaluable for data analysis, making complex computations straightforward and intuitive. - Users appreciate the **powerful data analysis and visualization tools** of MATLAB, enhancing their research and presentation capabilities. - Users appreciate the **excellent visualization tools** in MATLAB, making data presentation clear and professional. - Users value the **variety of toolboxes** in MATLAB, enhancing their efficiency in complex data analysis and modeling. - Users value MATLAB for its **effective simulation capabilities** , enabling quick transitions from ideas to workable solutions. **Cons:** - Users find MATLAB **expensive** , particularly due to high licensing fees and additional costs for toolboxes and features. - Users often face **slow performance** with MATLAB, especially on less powerful machines and during complex simulations. - Users report **high system requirements** for MATLAB, leading to slower performance and longer execution times on less powerful machines. - Users often find the **expensive licensing** of MATLAB a barrier, especially for individuals and small companies. - Users experience **lagging performance** with MATLAB, especially during high-resource tasks and large-scale simulations. #### What Are Recent G2 Reviews of MATLAB? **"[Powerful Math and Visualization Tools That Boost Productivity](https://www.g2.com/survey_responses/matlab-review-12811316)"** **Rating:** 5.0/5.0 stars *— Dipendu M.* [Read full review](https://www.g2.com/survey_responses/matlab-review-12811316) --- **"[A Robust Powerhouse for Advanced Engineering Simulations and Modeling](https://www.g2.com/survey_responses/matlab-review-12689149)"** **Rating:** 4.0/5.0 stars *— Wafa M.* [Read full review](https://www.g2.com/survey_responses/matlab-review-12689149) --- #### What Are G2 Users Discussing About MATLAB? - [What is MATLAB used for?](https://www.g2.com/discussions/what-is-matlab-used-for) - 1 comment - [Can I use Matlab for free?](https://www.g2.com/discussions/can-i-use-matlab-for-free) - 3 comments - [What is Matlab written in?](https://www.g2.com/discussions/what-is-matlab-written-in) - 1 comment - [Is Matlab a programming language or software?](https://www.g2.com/discussions/is-matlab-a-programming-language-or-software) - 1 comment - [What is Matlab software used for?](https://www.g2.com/discussions/what-is-matlab-software-used-for) - 1 comment ### 7. [Hex](https://www.g2.com/products/hex-tech-hex/reviews) Hex is the world’s favorite AI Analytics platform. With Hex, anyone can explore data using natural language, with or without code, all on trusted context, in one AI-powered platform. Get started now \> https://app.hex.tech/signup?source=g2 Get a demo \> https://hex.tech/request-a-demo/?source=g2 **Average Rating:** 4.5/5.0 **Total Reviews:** 401 **How Do G2 Users Rate Hex?** - **Application:** 6.9/10 (Category avg: 8.5/10) - **Managed Service:** 6.8/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 5.1/10 (Category avg: 8.2/10) - **Ease of Admin:** 9.0/10 (Category avg: 8.5/10) **Who Is the Company Behind Hex?** - **Seller:** [Hex Tech](https://www.g2.com/sellers/hex-tech) - **Company Website:** https://hex.tech/ - **Year Founded:** 2019 - **HQ Location:** San Francisco, US - **Twitter:** @_hex_tech (6,982 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/hex-technologies/ (249 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Analyst, Data Scientist - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 53% Mid-Market, 22% Small-Business #### What Are Hex's Pros and Cons? **Pros:** - Ease of Use (114 reviews) - SQL Queries (73 reviews) - Data Management (69 reviews) - SQL Querying (66 reviews) - Data Analysis (55 reviews) **Cons:** - Limited Features (43 reviews) - Missing Features (39 reviews) - Lacking Features (35 reviews) - Slow Performance (28 reviews) - Data Management Issues (27 reviews) ### What Do G2 Reviewers Say About Hex? *AI-generated summary from verified user reviews* **Pros:** - Users enjoy the **user-friendly ease of use** of Hex, making integration and setup smooth and efficient. - Users appreciate the **seamless integration of SQL and Python** in Hex, enhancing their analytical experience significantly. - Users appreciate the **seamless data integration** and intuitive tools of Hex, enhancing collaboration and analysis efficiency. - Users appreciate the **seamless integration of SQL and Python** in Hex, enhancing their analytical experience efficiently. - Users appreciate the **seamless data analysis capabilities** of Hex, combining ease of use with powerful tools for collaboration. **Cons:** - Users express concerns about Hex's **limited features** , finding them insufficient compared to more robust BI tools. - Users note the **missing features** in Hex, such as saved query results and improved data visualization options. - Users feel that Hex's **lack of essential features** hinders its dashboarding capabilities and overall effectiveness. - Users experience **slow performance** in Hex, especially in virtual machines, impacting usability and functionality. - Users face **data management issues** with Hex, including unreliable notebook runs and GPU integration challenges affecting productivity. #### What Are Recent G2 Reviews of Hex? **"[Effortless Data Analysis with Powerful AI](https://www.g2.com/survey_responses/hex-review-12262172)"** **Rating:** 5.0/5.0 stars *— Hunter P.* [Read full review](https://www.g2.com/survey_responses/hex-review-12262172) --- **"[Amazing AI and SQL Autocomplete That Speeds Up My Work](https://www.g2.com/survey_responses/hex-review-12687305)"** **Rating:** 4.0/5.0 stars *— Paco R.* [Read full review](https://www.g2.com/survey_responses/hex-review-12687305) --- #### What Are G2 Users Discussing About Hex? - [What is Hex Technologies used for?](https://www.g2.com/discussions/what-is-hex-technologies-used-for) ### 8. [Deepnote](https://www.g2.com/products/deepnote/reviews) Deepnote is a data workspace where agents and humans work together. It's designed to simplify data exploration, accelerate analysis, and quickly deliver actionable insights for you and your team. Unlike outdated tools such as Jupyter, Deepnote is built with the next decade in mind. Deepnote gives anyone working with data superpowers. It unifies your data workflow through an integrated semantic layer, preparing your data for advanced AI applications. You can also leverage our AI data copilot to chat with your data, create charts, write code, or turn your AI notebooks into fully-fledged data dashboards or apps. Combine data, SQL or Python code, and visualizations side-by-side on a flexible canvas - enhanced with cutting-edge AI reasoning models. 🤖 Analyze with AI • Generate code and visualizations by describing your goal. • Auto-write, run, and debug code with AI. • Move faster with context-aware AI suggestions. 🔗 Unify • Connect to 60+ data sources like BigQuery, Snowflake, and PostgreSQL. • Combine Python and SQL in one notebook. • Build reusable ETL, analytics, and metric modules. • Create a semantic layer with shared definitions and trusted metrics. ⚖️ Scale • Instantly boost compute power, more included than Colab. • Schedule jobs and get notified with fresh results. • Organize work in projects and folders for team clarity. • Manage workflows via REST API. 🚀 Launch • Turn notebooks into dashboards or data apps, natively or with Streamlit. • Let users explore data with interactive inputs. • Share secure, live apps in one click. **Average Rating:** 4.5/5.0 **Total Reviews:** 378 **How Do G2 Users Rate Deepnote?** - **Application:** 8.0/10 (Category avg: 8.5/10) - **Managed Service:** 7.9/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 7.2/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.8/10 (Category avg: 8.5/10) **Who Is the Company Behind Deepnote?** - **Seller:** [Deepnote](https://www.g2.com/sellers/deepnote) - **Company Website:** https://www.deepnote.com - **Year Founded:** 2019 - **HQ Location:** San Francisco , US - **Twitter:** @DeepnoteHQ (5,239 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/deepnote (17 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Student, Data Analyst - **Top Industries:** Computer Software, Higher Education - **Company Size:** 68% Small-Business, 25% Mid-Market #### What Are Deepnote's Pros and Cons? **Pros:** - Ease of Use (170 reviews) - Collaboration (93 reviews) - Easy Integrations (76 reviews) - Team Collaboration (76 reviews) - Integrations (62 reviews) **Cons:** - Slow Performance (47 reviews) - Limited Features (32 reviews) - Data Management Issues (29 reviews) - Lacking Features (25 reviews) - Lagging Performance (25 reviews) ### What Do G2 Reviewers Say About Deepnote? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **ease of use** of Deepnote, enabling efficient collaboration and simplifying complex tasks effortlessly. - Users love the **seamless collaboration** in Deepnote, enhancing teamwork through real-time editing and organized project structures. - Users appreciate the **easy integrations** of Deepnote, enhancing their workflow with seamless connections to various tools. - Users value the **seamless team collaboration** in Deepnote, enhancing productivity and efficiency in data and engineering projects. - Users value the **wide range of integrations** in Deepnote, facilitating seamless data access and enhancing project efficiency. **Cons:** - Users experience **slow performance** with large datasets, which can hinder their analysis and overall usability. - Users note the **limited features** of Deepnote, lack of SQL joins and Git integration hinder their experience. - Users report **data management issues** in Deepnote, particularly with project awareness and SQL compatibility challenges. - Users note a **lack of features** in Deepnote, particularly in project awareness and visualization tools. - Users experience **lagging performance** , especially when handling large datasets, affecting their overall productivity and collaboration. #### What Are Recent G2 Reviews of Deepnote? **"[Real-Time Collaboration That Makes Lab Work Easy](https://www.g2.com/survey_responses/deepnote-review-13100282)"** **Rating:** 5.0/5.0 stars *— Gabriel P.* [Read full review](https://www.g2.com/survey_responses/deepnote-review-13100282) --- **"[Clarity for complex nutrition work](https://www.g2.com/survey_responses/deepnote-review-12699174)"** **Rating:** 5.0/5.0 stars *— Marine A.* [Read full review](https://www.g2.com/survey_responses/deepnote-review-12699174) --- #### What Are G2 Users Discussing About Deepnote? - [How do you use a deep note?](https://www.g2.com/discussions/how-do-you-use-a-deep-note) - [Is Deepnote open source?](https://www.g2.com/discussions/is-deepnote-open-source) - [Is Deepnote good?](https://www.g2.com/discussions/is-deepnote-good) - 1 comment - [Is Deepnote better than Colab?](https://www.g2.com/discussions/is-deepnote-better-than-colab) - 1 comment ### 9. [IBM watsonx.data](https://www.g2.com/products/ibm-watsonx-data/reviews) IBM® watsonx.data® helps you access, integrate and understand all your data —structured and unstructured—across any environment. It optimizes workloads for price and performance while enforcing consistent governance across sources, formats and teams. Watch the demo to learn how watsonx.data empowers you to build gen AI apps and powerful AI agents. Free Trial available: https://ibm.biz/Watsonx-data\_Trial **Average Rating:** 4.4/5.0 **Total Reviews:** 159 **How Do G2 Users Rate IBM watsonx.data?** - **Application:** 5.8/10 (Category avg: 8.5/10) - **Managed Service:** 7.2/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 7.5/10 (Category avg: 8.2/10) - **Ease of Admin:** 7.8/10 (Category avg: 8.5/10) **Who Is the Company Behind IBM watsonx.data?** - **Seller:** [IBM](https://www.g2.com/sellers/ibm) - **Company Website:** https://www.ibm.com - **Year Founded:** 1911 - **HQ Location:** Armonk, New York, United States - **Twitter:** @IBMSecurity (74,660 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (328,202 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Software Engineer, CEO - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 34% Small-Business, 32% Enterprise #### What Are IBM watsonx.data's Pros and Cons? **Pros:** - Ease of Use (67 reviews) - Features (47 reviews) - Data Management (41 reviews) - Integrations (33 reviews) - Analytics (31 reviews) **Cons:** - Learning Curve (38 reviews) - Complexity (25 reviews) - Expensive (20 reviews) - Difficult Setup (17 reviews) - Difficulty (17 reviews) ### What Do G2 Reviewers Say About IBM watsonx.data? *AI-generated summary from verified user reviews* **Pros:** - Users admire the **ease of use** of IBM watsonx.data, streamlining data management and enhancing analytics efficiency. - Users value the **seamless integration** of data types and features in IBM watsonx.data, enhancing their analytics experience. - Users value the **organized data management** of IBM watsonx.data, streamlining workflow and improving efficiency across various tasks. - Users value the **seamless data source integration** of IBM watsonx.data, enhancing overall efficiency and usability. - Users value the **unification of data across hybrid environments** , enhancing flexibility and decision-making for AI applications. **Cons:** - Users face a **steep learning curve** with IBM watsonx.data, making initial setup and navigation challenging for newcomers. - Users find the **complexity of setup and management** for IBM watsonx.data a barrier to accessibility for new users. - Users find the **pricing to be high** compared to other options, making it less accessible for smaller businesses. - Users face a **difficult setup** process with IBM watsonx.data, requiring time and effort to configure and integrate properly. - Users find the **difficulty in performance tuning** with IBM watsonx.data challenging, especially for beginners and inexperienced teams. #### What Are Recent G2 Reviews of IBM watsonx.data? **"[Powerful Query Performance and Governance, But a Steep Onboarding Learning Curve](https://www.g2.com/survey_responses/ibm-watsonx-data-review-12836202)"** **Rating:** 4.0/5.0 stars *— Arkajit D.* [Read full review](https://www.g2.com/survey_responses/ibm-watsonx-data-review-12836202) --- **"[Unified Data Management with Learning Curve](https://www.g2.com/survey_responses/ibm-watsonx-data-review-12817742)"** **Rating:** 5.0/5.0 stars *— Anchal P.* [Read full review](https://www.g2.com/survey_responses/ibm-watsonx-data-review-12817742) --- ### 10. [Anaconda Core](https://www.g2.com/products/anaconda-core/reviews) Anaconda Platform is a unified enterprise AI development platform that helps data scientists, AI developers, and platform engineers build, secure, deploy, and observe AI workloads from development to production. The platform addresses critical challenges enterprises face when scaling open-source AI initiatives, including environment complexity, security vulnerabilities, deployment failures, and governance requirements across cloud and on-premises infrastructure. The platform combines trusted Python package distribution with enterprise-grade governance controls, enabling organizations to accelerate AI innovation while maintaining security and compliance. Data scientists access over 12,000 pre-vetted, compatible open-source packages through Anaconda's secure distribution, eliminating dependency conflicts and environment drift that typically slow deployment cycles. Platform administrators gain centralized management, role-based access controls, and automated vulnerability detection across all AI workloads. Key capabilities include: Trusted Distribution - Pre-validated Python packages with signature verification, software bills of materials (SBOMs), and guaranteed uptime SLAs reduce supply chain security risks Secure Governance - Automated CVE scanning, package filtering, audit logs, and compliance tracking for GDPR, HIPAA, and CCPA requirements enable teams to move fast without compromising security Developer Velocity - Pre-configured environments, one-command setup, and automatic dependency resolution eliminate debugging time so developers focus on building solutions Production-Ready AI - High-performance runtimes and proven deployment workflows ensure what works locally runs reliably at scale, bridging the gap between experimentation and production Actionable Insights - Real-time telemetry and usage metrics across packages, environments, and models provide visibility for data-driven optimization decisions Organizations using Anaconda Platform report 60% reduced risk of security breaches, 80% efficiency improvement in package security management, and significant time savings by eliminating manual package vetting processes. **Average Rating:** 4.5/5.0 **Total Reviews:** 235 **How Do G2 Users Rate Anaconda Core?** - **Application:** 8.9/10 (Category avg: 8.5/10) - **Managed Service:** 8.6/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.5/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.7/10 (Category avg: 8.5/10) **Who Is the Company Behind Anaconda Core?** - **Seller:** [Anaconda, Inc.](https://www.g2.com/sellers/anaconda-inc) - **Year Founded:** 2012 - **HQ Location:** Austin, Texas - **Twitter:** @anacondainc (83,629 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/25029553/ (580 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Student, Software Engineer - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 38% Small-Business, 25% Enterprise #### What Are Anaconda Core's Pros and Cons? **Pros:** - Ease of Use (74 reviews) - Setup Ease (39 reviews) - Efficiency (27 reviews) - Intuitive (25 reviews) - Coding Ease (23 reviews) **Cons:** - Data Management Issues (11 reviews) - Slow Performance (11 reviews) - Lacking Features (10 reviews) - Limited Features (9 reviews) - Limited Storage (9 reviews) ### What Do G2 Reviewers Say About Anaconda Core? *AI-generated summary from verified user reviews* **Pros:** - Users commend the **ease of use** of Anaconda Core, making installation and management of packages effortless across platforms. - Users commend the **easy setup** of Anaconda Core, facilitating efficient project initiation and management in data science. - Users find Anaconda Core to enhance **efficiency** in development through seamless integration and easy package management. - Users value the **intuitive interface** of Anaconda Core, facilitating efficient management and access to necessary tools effortlessly. - Users value the **coding ease** provided by Anaconda, enjoying effortless access to essential tools and seamless environment management. **Cons:** - Users face **data management issues** with Anaconda Core, including high memory usage and absence of automatic backups. - Users report **slow performance** in Anaconda Core, especially during package installations and when using older hardware. - Users find Anaconda Core **lacking features** , particularly in the Free Tier and accessibility of new tools and insights. - Users feel the **limited features** of Anaconda Core hinder usability and lack essential tools for effective development. - Users find the **heavy installation size** of Anaconda Core cumbersome, particularly with storage constraints and slow download times. #### What Are Recent G2 Reviews of Anaconda Core? **"[All-in-One Toolkit for Data Science Workflows](https://www.g2.com/survey_responses/anaconda-core-review-12706297)"** **Rating:** 4.5/5.0 stars *— Melissa F.* [Read full review](https://www.g2.com/survey_responses/anaconda-core-review-12706297) --- **"[Great for Learning, Needs a Friendlier Interface](https://www.g2.com/survey_responses/anaconda-core-review-12253770)"** **Rating:** 4.0/5.0 stars *— Firdavs S.* [Read full review](https://www.g2.com/survey_responses/anaconda-core-review-12253770) --- #### What Are G2 Users Discussing About Anaconda Core? - [Is Anaconda free for companies?](https://www.g2.com/discussions/anaconda-is-anaconda-free-for-companies) - 2 comments, 1 upvote - [Is Anaconda free for companies?](https://www.g2.com/discussions/is-anaconda-free-for-companies) - 1 comment, 1 upvote - [Is Anaconda good for machine learning?](https://www.g2.com/discussions/anaconda-is-anaconda-good-for-machine-learning) - 1 comment, 1 upvote - [Is Anaconda good for machine learning?](https://www.g2.com/discussions/is-anaconda-good-for-machine-learning) - 1 comment - [What is Anaconda software used for?](https://www.g2.com/discussions/anaconda-what-is-anaconda-software-used-for) ### 11. [Posit Team](https://www.g2.com/products/posit-team/reviews) Posit is a Public Benefit Corporation building open-source software and an enterprise data science platform. We created the RStudio IDE, Shiny, Positron, and Quarto — tools used by millions of data scientists, machine learning engineers, and researchers worldwide, including teams at 25% of the Fortune Global 100. Our commercial products help organizations put those tools into production: Posit Workbench provides centralized development environments supporting Positron, RStudio, VS Code, and Jupyter; Posit Connect handles publishing and deployment for Shiny, AI applications, Streamlit, Dash, FastAPI, Flask, Bokeh, and more; and Posit Package Manager provides security-compliant package management for R and Python. **Average Rating:** 4.5/5.0 **Total Reviews:** 567 **How Do G2 Users Rate Posit Team?** - **Application:** 8.4/10 (Category avg: 8.5/10) - **Managed Service:** 8.3/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.6/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Posit Team?** - **Seller:** [Posit](https://www.g2.com/sellers/posit) - **Year Founded:** 2009 - **HQ Location:** Boston, US - **Twitter:** @posit_pbc (120,874 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1978648/ (448 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Research Assistant, Graduate Research Assistant - **Top Industries:** Higher Education, Information Technology and Services - **Company Size:** 48% Enterprise, 26% Mid-Market #### What Are Posit Team's Pros and Cons? **Pros:** - Ease of Use (13 reviews) - Features (9 reviews) - Open Source (7 reviews) - Customer Support (5 reviews) - Easy Integrations (5 reviews) **Cons:** - Slow Performance (7 reviews) - Learning Curve (4 reviews) - Performance Issues (4 reviews) - Steep Learning Curve (4 reviews) - Lagging Performance (3 reviews) ### What Do G2 Reviewers Say About Posit Team? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **ease of use** of Posit Team, as it simplifies workflows and enhances productivity across devices. - Users value Posit's **market leadership and seamless integrations** , appreciating its ease of use and comprehensive support. - Users value Posit's **commitment to open source software** , enhancing usability and integration in their applications. - Users appreciate the **excellent customer support** from Posit, enhancing their experience with timely assistance when needed. - Users value the **easy integrations** of Posit Team, making analytics tasks seamless and efficient across various tools. **Cons:** - Users experience **slow performance** with large datasets, impacting workflow and requiring significant system resources for optimal use. - Users face a **steep learning curve** with Posit, making it challenging for new users to adapt quickly. - Users report **performance issues** , especially with larger datasets, requiring more resources and frequent updates. - Users face a **steep learning curve** with Posit Team, making it challenging for new users to adapt quickly. - Users experience **lagging performance** with Posit, particularly when handling large datasets, impacting productivity and workflow. #### What Are Recent G2 Reviews of Posit Team? **"[Posit Team Makes Biostatistical Work Reproducible, Collaborative, and Secure](https://www.g2.com/survey_responses/posit-team-review-12977958)"** **Rating:** 5.0/5.0 stars *— Donald S.* [Read full review](https://www.g2.com/survey_responses/posit-team-review-12977958) --- **"[Exceptional Open-Source Data Science Tools with Great Documentation and R/Python Support](https://www.g2.com/survey_responses/posit-team-review-13022732)"** **Rating:** 5.0/5.0 stars *— Omer F. Y.* [Read full review](https://www.g2.com/survey_responses/posit-team-review-13022732) --- #### What Are G2 Users Discussing About Posit Team? - [What is the difference between RStudio desktop and Rstudio server?](https://www.g2.com/discussions/what-is-the-difference-between-rstudio-desktop-and-rstudio-server) - [What is the difference between R and R studio?](https://www.g2.com/discussions/what-is-the-difference-between-r-and-r-studio) - [Is R Studio free?](https://www.g2.com/discussions/is-r-studio-free) - [Which software is used for R programming?](https://www.g2.com/discussions/which-software-is-used-for-r-programming) - 1 comment ### 12. [IBM watsonx.ai](https://www.g2.com/products/ibm-watsonx-ai/reviews) Watsonx.ai is part of the IBM watsonx platform that brings together new generative AI capabilities, powered by foundation models and traditional machine learning into a powerful studio spanning the AI lifecycle. With watsonx.ai, you can build, train, validate, tune and deploy generative AI, foundation models and machine learning capabilities with ease and build AI applications in a fraction of the time with a fraction of the data. **Average Rating:** 4.4/5.0 **Total Reviews:** 134 **How Do G2 Users Rate IBM watsonx.ai?** - **Application:** 8.8/10 (Category avg: 8.5/10) - **Managed Service:** 8.5/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.6/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.5/10 (Category avg: 8.5/10) **Who Is the Company Behind IBM watsonx.ai?** - **Seller:** [IBM](https://www.g2.com/sellers/ibm) - **Company Website:** https://www.ibm.com - **Year Founded:** 1911 - **HQ Location:** Armonk, New York, United States - **Twitter:** @IBMSecurity (74,660 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (328,202 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Consultant - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 40% Small-Business, 32% Enterprise #### What Are IBM watsonx.ai's Pros and Cons? **Pros:** - Ease of Use (67 reviews) - Model Variety (29 reviews) - AI Integration (28 reviews) - Features (26 reviews) - AI Capabilities (23 reviews) **Cons:** - Difficult Learning (20 reviews) - Complexity (19 reviews) - Learning Curve (19 reviews) - Expensive (17 reviews) - Complex Setup (15 reviews) ### What Do G2 Reviewers Say About IBM watsonx.ai? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **ease of use** of IBM watsonx.ai, valuing its intuitive interface and seamless integration. - Users value the **model variety** of IBM watsonx.ai, enabling custom training on diverse machine learning models. - Users value the **integrated studio for enterprise-grade AI** , enhancing reliability and seamless workflow integration for effective decisions. - Users appreciate the **integrated studio and enterprise-grade AI** of IBM watsonx.ai for its practical, scalable solutions. - Users appreciate the **integrated enterprise-grade AI** of IBM watsonx.ai, enabling seamless workflows and informed decision-making. **Cons:** - Users find the **difficult learning** curve of IBM watsonx.ai can hinder accessibility for less technical individuals. - Users note the **complex setup** of IBM watsonx.ai, making it challenging for beginners and small teams. - Users struggle with the **steep learning curve** of IBM watsonx.ai, making it challenging for non-technical teams to utilize effectively. - Users find the product **expensive** , particularly for small teams, making it challenging to justify its cost. - Users find the **complex setup** of IBM watsonx.ai frustrating, especially for small teams and new users. #### What Are Recent G2 Reviews of IBM watsonx.ai? **"[Enterprise-Ready Prompt Lab for Comparing Models and Building Project-Based AI Solutions](https://www.g2.com/survey_responses/ibm-watsonx-ai-review-13088968)"** **Rating:** 4.5/5.0 stars *— Aleksander M.* [Read full review](https://www.g2.com/survey_responses/ibm-watsonx-ai-review-13088968) --- **"[Enterprise-Ready AI with Strong Governance and Flexible Model Support](https://www.g2.com/survey_responses/ibm-watsonx-ai-review-12773148)"** **Rating:** 4.0/5.0 stars *— Arkajit D.* [Read full review](https://www.g2.com/survey_responses/ibm-watsonx-ai-review-12773148) --- ### 13. [Deep Learning VM Image](https://www.g2.com/products/deep-learning-vm-image/reviews) Deep Learning VM Images are pre-configured virtual machine images optimized for data science and machine learning tasks. These images come with essential machine learning frameworks and tools pre-installed, enabling users to deploy and scale machine learning models efficiently on Google Cloud's infrastructure. Key Features and Functionality: - Pre-installed Frameworks: Support for TensorFlow Enterprise, TensorFlow, PyTorch, and generic high-performance computing, catering to various machine learning needs. - Operating System Options: Based on Debian 11 and Ubuntu 22.04, providing flexibility and compatibility with different environments. - Comprehensive Python Environment: Includes Python 3.10 with a suite of libraries such as NumPy, SciPy, Matplotlib, Pandas, NLTK, Pillow, scikit-image, OpenCV, and scikit-learn, facilitating a robust development experience. - JupyterLab Integration: Offers JupyterLab notebook environments for rapid prototyping and interactive development. - GPU Acceleration: Equipped with the latest NVIDIA drivers and packages, including CUDA 11.x and 12.x, CuDNN, and NCCL, to leverage GPU capabilities for accelerated computation. Primary Value and User Solutions: Deep Learning VM Images streamline the setup process for machine learning projects by providing ready-to-use environments with pre-installed frameworks and tools. This reduces the time and effort required for configuration, allowing data scientists and machine learning practitioners to focus on model development and experimentation. The integration with Google Cloud's scalable infrastructure ensures that users can efficiently manage and scale their machine learning workloads, whether they require CPU or GPU resources. Regular updates and community support further enhance the reliability and performance of these VM images, making them a valuable resource for accelerating machine learning initiatives. **Average Rating:** 4.4/5.0 **Total Reviews:** 48 **How Do G2 Users Rate Deep Learning VM Image?** - **Application:** 8.8/10 (Category avg: 8.5/10) - **Managed Service:** 8.4/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.5/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.9/10 (Category avg: 8.5/10) **Who Is the Company Behind Deep Learning VM Image?** - **Seller:** [Google](https://www.g2.com/sellers/google) - **Year Founded:** 1998 - **HQ Location:** Mountain View, CA - **Twitter:** @google (31,899,995 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (341,888 employees on LinkedIn®) - **Ownership:** NASDAQ:GOOG **Who Uses This Product?** - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 51% Small-Business, 31% Mid-Market #### What Are Deep Learning VM Image's Pros and Cons? **Pros:** - Ease of Use (28 reviews) - Setup Ease (15 reviews) - Features (14 reviews) - Easy Integrations (11 reviews) - Easy Setup (11 reviews) **Cons:** - Expensive (15 reviews) - Cost (8 reviews) - Learning Difficulty (7 reviews) - Difficult Learning (6 reviews) - Dependency Issues (5 reviews) ### What Do G2 Reviewers Say About Deep Learning VM Image? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **ease of use** of the Deep Learning VM Image, saving time for teams new to ML. - Users love the **setup ease** of Deep Learning VM Image, allowing quick initiation of ML projects without headaches. - Users appreciate the **pre-configured setup and GPU optimizations** of Deep Learning VM Image, enhancing efficiency and saving time. - Users value the **easy integrations** of Deep Learning VM Image, enhancing their productivity with seamless cloud connectivity. - Users value the **easy setup** of Deep Learning VM Image, allowing immediate focus on model training and development. **Cons:** - Users find the **pricing to be significantly higher** compared to general-purpose VMs, affecting affordability. - Users highlight the **high costs** of the Deep Learning VM Image, especially for GPU/TPU usage and continuous operation. - Users face a **steep learning curve** with Google Deep Learning VM, making it overwhelming for new users. - Users note a **difficult learning curve** , making it challenging for beginners to fully utilize the Deep Learning VM Image. - Users find the **dependency on Google Cloud providers** limiting, hindering flexibility and multi-cloud strategies. #### What Are Recent G2 Reviews of Deep Learning VM Image? **"[A Turnkey Powerhouse for Deep Learning—Ready from the Start](https://www.g2.com/survey_responses/deep-learning-vm-image-review-11362384)"** **Rating:** 4.5/5.0 stars *— Sergio R.* [Read full review](https://www.g2.com/survey_responses/deep-learning-vm-image-review-11362384) --- **"[Efficient for Deep Learning, Needs Streamlined Dependency Management](https://www.g2.com/survey_responses/deep-learning-vm-image-review-11832751)"** **Rating:** 4.0/5.0 stars *— Abhishek J.* [Read full review](https://www.g2.com/survey_responses/deep-learning-vm-image-review-11832751) --- ### 14. [Alteryx](https://www.g2.com/products/alteryx/reviews) Alteryx, through it's Alteryx One platform, helps enterprises transform complex, disconnected data into a clean, AI-ready state. Whether you’re creating financial forecasts, analyzing supplier performance, segmenting customer data, analyzing employee retention, or building competitive AI applications from your proprietary data, Alteryx One makes it easy to cleanse, blend, and analyze data to unlock the unique insights that drive impactful decisions. AI-Guided Analytics Alteryx automates and simplifies every stage of data preparation and analysis, from validation and enrichment to predictive analytics and automated insights. Incorporate generative AI directly into your workflows to streamline complex data tasks and generate insights faster. Unmatched flexibility, whether you prefer code-free workflows, natural language commands, or low-code options, Alteryx adapts to your needs. Trusted. Secure. Enterprise-Ready. Alteryx is trusted by over half of the Global 2000 and 19 of the top 20 global banks. With built-in automation, governance, and security, your workflows can scale and maintain compliance while delivering consistent results. And it doesn’t matter if your systems are on-premises, hybrid, or in the cloud; Alteryx fits effortlessly into your infrastructure. Easy to Use. Deeply Connected. What truly sets Alteryx apart is our focus on efficiency and ease of use for analysts and our active community of 700,000 Alteryx users to support you at every step of your journey. With seamless integration to data everywhere including platforms like Databricks, Snowflake, AWS, Google, SAP, and Salesforce, our platform helps unify siloed data and accelerate getting to insights. Visit Alteryx.com for more information, and to start your free trial. **Average Rating:** 4.6/5.0 **Total Reviews:** 844 **How Do G2 Users Rate Alteryx?** - **Application:** 8.7/10 (Category avg: 8.5/10) - **Managed Service:** 7.9/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 7.9/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Alteryx?** - **Seller:** [Alteryx](https://www.g2.com/sellers/alteryx) - **Company Website:** https://www.alteryx.com - **Year Founded:** 1997 - **HQ Location:** Irvine, CA - **Twitter:** @alteryx (26,149 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/903031/ (2,304 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Analyst, Analyst - **Top Industries:** Financial Services, Accounting - **Company Size:** 63% Enterprise, 21% Mid-Market #### What Are Alteryx's Pros and Cons? **Pros:** - Ease of Use (333 reviews) - Automation (148 reviews) - Intuitive (132 reviews) - Easy Learning (102 reviews) - Efficiency (102 reviews) **Cons:** - Expensive (88 reviews) - Learning Curve (80 reviews) - Missing Features (62 reviews) - Learning Difficulty (55 reviews) - Slow Performance (41 reviews) ### What Do G2 Reviewers Say About Alteryx? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **ease of use** of Alteryx, making data manipulation simple and accessible for everyone. - Users appreciate the **efficient automation** in Alteryx, significantly speeding up their data preparation and analysis tasks. - Users find Alteryx to be **very intuitive** , making technology accessible and easy to learn for everyone. - Users find Alteryx to be **easy to learn and use** , with intuitive tools that enhance workflow efficiency. - Users value the **efficiency** of Alteryx, effortlessly managing and processing data for streamlined analysis. **Cons:** - Users find Alteryx's licensing to be **expensive** , especially challenging for small teams and startups to justify costs. - Users note a **learning curve** for mastering advanced features, which can be challenging for beginners. - Users feel the **missing features** in Alteryx hinder performance, requiring additional tools like Tableau for effective reporting. - Users note a **steep learning curve** for Alteryx, particularly those accustomed to hardcore SQL practices. - Users report that **slow performance** with large workflows hampers analysis and complicates data wrangling tasks. #### What Are Recent G2 Reviews of Alteryx? **"[Alteryx Streamlines Data Prep with an Intuitive Drag-and-Drop Workflow Builder](https://www.g2.com/survey_responses/alteryx-review-13000974)"** **Rating:** 4.5/5.0 stars *— Ankit K.* [Read full review](https://www.g2.com/survey_responses/alteryx-review-13000974) --- **"[Intuitive Drag-and-Drop Analytics That Speeds Up Data Prep and Insights](https://www.g2.com/survey_responses/alteryx-review-12983224)"** **Rating:** 4.5/5.0 stars *— Akhil S.* [Read full review](https://www.g2.com/survey_responses/alteryx-review-12983224) --- ### 15. [TensorFlow](https://www.g2.com/products/tensorflow/reviews) TensorFlow is an open-source machine learning library developed by the Google Brain Team, designed to facilitate the creation, training, and deployment of machine learning models across various platforms. It provides a comprehensive ecosystem that supports tasks ranging from simple data flow graphs to complex neural networks, enabling developers and researchers to build and deploy machine learning applications efficiently. Key Features and Functionality: - Flexible Architecture: TensorFlow's architecture allows for deployment across multiple platforms, including CPUs, GPUs, and TPUs, and supports various operating systems such as Linux, macOS, Windows, Android, and JavaScript. - Multiple Language Support: While primarily offering a Python API, TensorFlow also provides support for other languages, including C++, Java, and JavaScript, catering to a diverse developer community. - High-Level APIs: TensorFlow includes high-level APIs like Keras, which simplify the process of building and training models, making machine learning more accessible to beginners and efficient for experts. - Eager Execution: This feature allows for immediate evaluation of operations, facilitating intuitive debugging and dynamic graph building. - Distributed Computing: TensorFlow supports distributed training, enabling the scaling of machine learning models across multiple devices and servers without significant code modifications. Primary Value and Solutions Provided: TensorFlow addresses the challenges of developing and deploying machine learning models by offering a unified, scalable, and flexible platform. It streamlines the workflow from model conception to deployment, reducing the complexity associated with machine learning projects. By supporting a wide range of platforms and languages, TensorFlow empowers users to implement machine learning solutions in diverse environments, from research labs to production systems. Its comprehensive suite of tools and libraries accelerates the development process, fosters innovation, and enables the creation of sophisticated models that can tackle real-world problems effectively. **Average Rating:** 4.5/5.0 **Total Reviews:** 136 **How Do G2 Users Rate TensorFlow?** - **Application:** 8.7/10 (Category avg: 8.5/10) - **Managed Service:** 8.4/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.7/10 (Category avg: 8.2/10) - **Ease of Admin:** 7.9/10 (Category avg: 8.5/10) **Who Is the Company Behind TensorFlow?** - **Seller:** [TensorFlow](https://www.g2.com/sellers/tensorflow) - **Year Founded:** 2016 - **HQ Location:** Centre Urbain Nord, TN - **Twitter:** @TensorFlow (377,398 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/tensorflow-tunis/ (1 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Software Engineer, Senior Software Engineer - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 50% Small-Business, 25% Mid-Market #### What Are TensorFlow's Pros and Cons? **Pros:** - Machine Learning (23 reviews) - AI Integration (19 reviews) - Ease of Use (19 reviews) - Model Variety (17 reviews) - Scalability (14 reviews) **Cons:** - Steep Learning Curve (25 reviews) - Complexity (7 reviews) - Difficult Learning (7 reviews) - Error Handling (6 reviews) - Slow Performance (5 reviews) ### What Do G2 Reviewers Say About TensorFlow? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **flexibility and scalability** of TensorFlow for building and deploying machine learning models effectively. - Users value TensorFlow for its **powerful AI integration** , enabling efficient model training and deployment across various platforms. - Users find TensorFlow's **ease of use** enhanced by Keras and strong community support for model building. - Users commend the **model variety** in TensorFlow, facilitating rapid prototyping and flexible deep learning development. - Users commend TensorFlow for its **scalability** , allowing seamless deployment of models across various platforms and environments. **Cons:** - Users find TensorFlow's **steep learning curve** difficult, particularly for beginners, making understanding and debugging challenging. - Users find TensorFlow's **complexity** challenging, especially for beginners and when dealing with GPU optimization issues. - Users find **learning TensorFlow difficult** , facing challenges with complex concepts and frustrating error messages while debugging. - Users struggle with **error handling** , finding error messages unclear and debugging TensorFlow challenging, especially for beginners. - Users experience **slow performance** with TensorFlow, especially when experimenting or handling smaller projects, affecting usability. #### What Are Recent G2 Reviews of TensorFlow? **"[My go to place to machine learning stuff](https://www.g2.com/survey_responses/tensorflow-review-11197207)"** **Rating:** 4.5/5.0 stars *— Leonardo S.* [Read full review](https://www.g2.com/survey_responses/tensorflow-review-11197207) --- **"[Scalable, Flexible, and Powerful: TensorFlow Boosts Deep Learning Productivity](https://www.g2.com/survey_responses/tensorflow-review-12523010)"** **Rating:** 5.0/5.0 stars *— Anbuselvam S.* [Read full review](https://www.g2.com/survey_responses/tensorflow-review-12523010) --- #### What Are G2 Users Discussing About TensorFlow? - [What are the core concepts of TensorFlow?](https://www.g2.com/discussions/what-are-the-core-concepts-of-tensorflow) - [What is TensorFlow and why it is used?](https://www.g2.com/discussions/what-is-tensorflow-and-why-it-is-used) - 2 comments - [What are advantages of TensorFlow?](https://www.g2.com/discussions/what-are-advantages-of-tensorflow) - [What is TensorFlow software used for?](https://www.g2.com/discussions/what-is-tensorflow-software-used-for) ### 16. [AWS Trainium](https://www.g2.com/products/aws-trainium/reviews) Get high performance for deep learning and generative AI training while lowering costs **Average Rating:** 4.6/5.0 **Total Reviews:** 15 **How Do G2 Users Rate AWS Trainium?** - **Application:** 8.8/10 (Category avg: 8.5/10) - **Managed Service:** 8.6/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.9/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.9/10 (Category avg: 8.5/10) **Who Is the Company Behind AWS Trainium?** - **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd) - **Year Founded:** 2006 - **HQ Location:** Seattle, WA - **Twitter:** @awscloud (2,232,483 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (147,094 employees on LinkedIn®) - **Ownership:** NASDAQ: AMZN **Who Uses This Product?** - **Company Size:** 56% Small-Business, 25% Enterprise #### What Are Recent G2 Reviews of AWS Trainium? **"[AI-Powered, User-Friendly, and Cost-Effective](https://www.g2.com/survey_responses/aws-trainium-review-12575881)"** **Rating:** 5.0/5.0 stars *— Nilesh T.* [Read full review](https://www.g2.com/survey_responses/aws-trainium-review-12575881) --- **"[Major Cost Savings with AWS Trainiun—Affordable Model Training](https://www.g2.com/survey_responses/aws-trainium-review-12711288)"** **Rating:** 5.0/5.0 stars *— Nitin J.* [Read full review](https://www.g2.com/survey_responses/aws-trainium-review-12711288) --- ### 17. [Amazon SageMaker](https://www.g2.com/products/amazon-sagemaker/reviews) Amazon SageMaker is a fully managed service that enables data scientists and developers to build, train, and deploy machine learning (ML) models at scale. It provides a comprehensive suite of tools and infrastructure, streamlining the entire ML workflow from data preparation to model deployment. With SageMaker, users can quickly connect to training data, select and optimize algorithms, and deploy models in a secure and scalable environment. Key Features and Functionality: - Integrated Development Environments (IDEs): SageMaker offers a unified, web-based interface with built-in IDEs, including JupyterLab and RStudio, facilitating seamless development and collaboration. - Pre-built Algorithms and Frameworks: It includes a selection of optimized ML algorithms and supports popular frameworks like TensorFlow, PyTorch, and Apache MXNet, allowing flexibility in model development. - Automated Model Tuning: SageMaker can automatically tune models to achieve optimal accuracy, reducing the time and effort required for manual adjustments. - Scalable Training and Deployment: The service manages the underlying infrastructure, enabling efficient training of models on large datasets and deploying them across auto-scaling clusters for high availability. - MLOps and Governance: SageMaker provides tools for monitoring, debugging, and managing ML models, ensuring robust operations and compliance with enterprise security standards. Primary Value and Problem Solved: Amazon SageMaker addresses the complexity and resource-intensive nature of developing and deploying ML models. By offering a fully managed environment with integrated tools and scalable infrastructure, it accelerates the ML lifecycle, reduces operational overhead, and enables organizations to derive insights and value from their data more efficiently. This empowers businesses to innovate rapidly and implement AI solutions without the need for extensive in-house expertise or infrastructure management. **Average Rating:** 4.3/5.0 **Total Reviews:** 53 **How Do G2 Users Rate Amazon SageMaker?** - **Application:** 8.6/10 (Category avg: 8.5/10) - **Managed Service:** 9.1/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 9.3/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.4/10 (Category avg: 8.5/10) **Who Is the Company Behind Amazon SageMaker?** - **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd) - **Year Founded:** 2006 - **HQ Location:** Seattle, WA - **Twitter:** @awscloud (2,232,483 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (147,094 employees on LinkedIn®) - **Ownership:** NASDAQ: AMZN **Who Uses This Product?** - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 34% Mid-Market, 32% Enterprise #### What Are Amazon SageMaker's Pros and Cons? **Pros:** - Ease of Use (3 reviews) - AI Integration (2 reviews) - Computing Power (2 reviews) - Efficiency (2 reviews) - Fast Processing (2 reviews) **Cons:** - Expensive (3 reviews) - Complexity (2 reviews) - Complexity Issues (2 reviews) - Learning Curve (2 reviews) - Difficult Learning (1 reviews) ### What Do G2 Reviewers Say About Amazon SageMaker? *AI-generated summary from verified user reviews* **Pros:** - Users find Amazon SageMaker's **ease of use** exceptional, enabling quick adaptation and efficient model training from the start. - Users value the **seamless AI integration** of Amazon SageMaker, which simplifies the entire machine learning lifecycle efficiently. - Users love the **impressive computing power** of Amazon SageMaker, drastically reducing model training time to minutes. - Users highlight the **efficiency** of Amazon SageMaker, significantly reducing model training time and simplifying experimentation. - Users enjoy the **fast processing** of Amazon SageMaker, significantly reducing model training time for efficient experimentation. **Cons:** - Users find Amazon SageMaker to be **expensive and complex** , particularly for long-running jobs and large deployments. - Users find the **complex pricing structure** of Amazon SageMaker challenging, often leading to unexpected high costs. - Users find the **complex pricing structure** of Amazon SageMaker can lead to unexpected costs and confusion. - Users find the **steep learning curve** of Amazon SageMaker challenging, particularly for those new to AWS services. - Users experience a **difficult learning curve** during the initial setup of Amazon SageMaker, impacting usability. #### What Are Recent G2 Reviews of Amazon SageMaker? **"[Fully Managed End-to-End ML in AWS with Powerful Distributed Training](https://www.g2.com/survey_responses/amazon-sagemaker-review-12853074)"** **Rating:** 4.0/5.0 stars *— Hem J.* [Read full review](https://www.g2.com/survey_responses/amazon-sagemaker-review-12853074) --- **"[A powerhouse for end-to-end ML, but be prepared for a steep learning curve](https://www.g2.com/survey_responses/amazon-sagemaker-review-12959870)"** **Rating:** 5.0/5.0 stars *— Lokesh S.* [Read full review](https://www.g2.com/survey_responses/amazon-sagemaker-review-12959870) --- #### What Are G2 Users Discussing About Amazon SageMaker? - [What is Amazon SageMaker used for?](https://www.g2.com/discussions/what-is-amazon-sagemaker-used-for) - [Is AWS SageMaker good?](https://www.g2.com/discussions/is-aws-sagemaker-good) - 1 upvote - [Who uses SageMaker?](https://www.g2.com/discussions/who-uses-sagemaker) - [How do you use Amazon SageMaker?](https://www.g2.com/discussions/how-do-you-use-amazon-sagemaker) - [What does Amazon SageMaker do?](https://www.g2.com/discussions/what-does-amazon-sagemaker-do) ### 18. [Wipro Holmes](https://www.g2.com/products/wipro-holmes/reviews) Wipro HOLMES is an Artificial Intelligence Platform that provide services for the development of digital virtual agents, predictive systems, cognitive process automation, visual computing applications, knowledge virtualization, robotics and drones to deliver cognitive enhancement to experience and productivity, accelerate process through automation and at the highest stage of maturity reach autonomous abilities **Average Rating:** 3.8/5.0 **Total Reviews:** 10 **How Do G2 Users Rate Wipro Holmes?** - **Application:** 7.1/10 (Category avg: 8.5/10) - **Managed Service:** 7.9/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 7.6/10 (Category avg: 8.2/10) - **Ease of Admin:** 6.1/10 (Category avg: 8.5/10) **Who Is the Company Behind Wipro Holmes?** - **Seller:** [Wipro](https://www.g2.com/sellers/wipro) - **Year Founded:** 1945 - **HQ Location:** Bangalore - **Twitter:** @Wipro (513,142 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1318/ (254,638 employees on LinkedIn®) - **Ownership:** WIT **Who Uses This Product?** - **Company Size:** 40% Enterprise, 30% Mid-Market #### What Are Wipro Holmes's Pros and Cons? **Pros:** - AI Integration (2 reviews) - Automation (2 reviews) - Efficiency (2 reviews) - Analysis Efficiency (1 reviews) - Cloud Computing (1 reviews) **Cons:** - Complex Interface (1 reviews) - Limited Customization (1 reviews) - Steep Learning Curve (1 reviews) ### What Do G2 Reviewers Say About Wipro Holmes? *AI-generated summary from verified user reviews* **Pros:** - Users greatly value the **seamless AI integration** of Wipro Holmes, enhancing efficiency and streamlining business processes effectively. - Users value Wipro Holmes for its **efficient automation** that enhances decision-making and streamlines complex business processes. - Users commend Wipro Holmes for its **remarkable efficiency** in automating tasks and streamlining business processes with AI. - Users appreciate the **analysis efficiency** of Wipro HOLMES, which enhances decision-making and streamlines data interactions. - Users appreciate the **remarkable efficiency through AI and automation** that Wipro Holmes provides, streamlining complex processes. **Cons:** - Users desire a more **intuitive interface** to enhance usability, particularly for non-technical team members managing automation. - Users feel the **limited customization** options of Wipro Holmes restrict their ability to tailor the product to their needs. - Users find the **steep learning curve** challenging, often needing technical assistance to utilize advanced features effectively. #### What Are Recent G2 Reviews of Wipro Holmes? **"[Efficient, Scalable AI with Room for UI Improvement](https://www.g2.com/survey_responses/wipro-holmes-review-11900962)"** **Rating:** 4.5/5.0 stars *— Manish A.* [Read full review](https://www.g2.com/survey_responses/wipro-holmes-review-11900962) --- **"[Wipro Holmes review](https://www.g2.com/survey_responses/wipro-holmes-review-11065144)"** **Rating:** 4.5/5.0 stars *— Deepak s.* [Read full review](https://www.g2.com/survey_responses/wipro-holmes-review-11065144) --- #### What Are G2 Users Discussing About Wipro Holmes? - [What is Wipro Holmes used for?](https://www.g2.com/discussions/what-is-wipro-holmes-used-for) - 1 comment ### 19. [Saturn Cloud](https://www.g2.com/products/saturn-cloud-saturn-cloud/reviews) Saturn Cloud is a portable AI platform that installs securely in any cloud account. Access the best GPUs with no Kubernetes configuration or DevOps, enable AI/ML teams to develop, deploy, and manage ML models with any stack, and give IT security the controls that work for your enterprise. Customers include NVIDIA, CFA Institute, Snowflake, Flatiron School, Nestle, and more. Get started for free at: saturncloud.io **Average Rating:** 4.8/5.0 **Total Reviews:** 320 **How Do G2 Users Rate Saturn Cloud?** - **Application:** 9.1/10 (Category avg: 8.5/10) - **Managed Service:** 9.1/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 9.1/10 (Category avg: 8.2/10) - **Ease of Admin:** 9.2/10 (Category avg: 8.5/10) **Who Is the Company Behind Saturn Cloud?** - **Seller:** [Saturn Cloud](https://www.g2.com/sellers/saturn-cloud) - **Year Founded:** 2018 - **HQ Location:** New York, US - **Twitter:** @saturn_cloud (3,279 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/saturn-cloud/ (41 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Scientist, Student - **Top Industries:** Computer Software, Higher Education - **Company Size:** 82% Small-Business, 12% Mid-Market #### What Are Saturn Cloud's Pros and Cons? **Pros:** - Ease of Use (18 reviews) - GPU Performance (13 reviews) - Computing Power (10 reviews) - Setup Ease (10 reviews) - Easy Integrations (8 reviews) **Cons:** - Expensive (6 reviews) - Missing Features (5 reviews) - Complexity Issues (4 reviews) - Poor Documentation (4 reviews) - Difficult Setup (3 reviews) ### What Do G2 Reviewers Say About Saturn Cloud? *AI-generated summary from verified user reviews* **Pros:** - Users find Saturn Cloud's **ease of use** remarkable, enabling hassle-free project setup and reliable performance. - Users benefit from the **excellent selection of top-tier NVIDIA GPUs** , enhancing performance for data science and machine learning projects. - Users value the **exceptional computing power** of Saturn Cloud, enabling efficient execution of complex deep learning models. - Users find the **setup ease** of Saturn Cloud exceptional, allowing hassle-free project initiation and management. - Users find the **easy integrations** of Saturn Cloud beneficial, simplifying setup and enhancing project collaboration. **Cons:** - Users find the **cost of Saturn Cloud to be a barrier** for entry, indicating a need for more affordable options. - Users feel the absence of **custom image support and extensive LLM selection** limits the platform's versatility and efficiency. - Users experience **complexity issues** with Saturn Cloud, particularly in pricing and advanced setup documentation for beginners. - Users struggle with the **poor documentation** , finding it lacks detail and guidance for beginners navigating the platform. - Users find Saturn Cloud's setup **difficult** , requiring time and effort to configure options like images and connections. #### What Are Recent G2 Reviews of Saturn Cloud? **"[An excellent platform to start your AI journey](https://www.g2.com/survey_responses/saturn-cloud-review-11404570)"** **Rating:** 5.0/5.0 stars *— Jianyi W.* [Read full review](https://www.g2.com/survey_responses/saturn-cloud-review-11404570) --- **"[Fast, Seamless GPU Environments with Dask & Jupyter Integration](https://www.g2.com/survey_responses/saturn-cloud-review-12270057)"** **Rating:** 4.0/5.0 stars *— Nataporn C.* [Read full review](https://www.g2.com/survey_responses/saturn-cloud-review-12270057) --- ### 20. [Cloudera](https://www.g2.com/products/cloudera/reviews) Cloudera is the only hybrid data and AI platform company that large organizations trust to bring AI to their data anywhere it lives. Unlike other providers, Cloudera delivers a consistent cloud experience that converges public clouds, on-prem data centers, and the edge, leveraging a proven open-source foundation. As the pioneer in big data, Cloudera empowers businesses to apply AI and assert control over 100% of their data, in all forms, improving security, governance, and real-time and predictive insights. The world’s largest brands across all industries rely on Cloudera to transform decision-making and ultimately boost bottom lines, safeguard against threats, and save lives. The Cloudera data and AI platform includes: Cloudera AI: Deploy and scale any AI model, anywhere. Cloudera brings compute to governed data where it lives for Private AI anywhere by design. Complete control, security, and governance of mission-critical data, models, agents, and inference ensure faster sovereign AI deployments. Cloudera Data-in-Motion: Make fast decisions from real-time data anywhere. Move data with any structure from any source to any destination seamlessly across hybrid environments, enabling in-the-moment business-critical decisions by processing and analyzing real-time data anywhere, from the edge to AI, as business happens. Cloudera Open Data Lakehouse: Process any data, anywhere, for actionable insights. Make smart decisions with an open data lakehouse powered by Apache Iceberg that delivers trusted, reliable, and unified data to fuel agents, AI applications, and analytics, improving collaboration, breaking silos, and simplifying sharing. Cloudera Unified Data Fabric: Unify security and governance across the entire data estate. Move beyond fragmented data management: Break down silos and connect disparate data sources intelligently and securely to provide a unified view of all organizational data and centralized end-to-end control across complex hybrid data environments. **Average Rating:** 4.1/5.0 **Total Reviews:** 131 **How Do G2 Users Rate Cloudera?** - **Application:** 9.5/10 (Category avg: 8.5/10) - **Managed Service:** 9.2/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 9.8/10 (Category avg: 8.2/10) - **Ease of Admin:** 7.9/10 (Category avg: 8.5/10) **Who Is the Company Behind Cloudera?** - **Seller:** [Cloudera](https://www.g2.com/sellers/cloudera) - **Company Website:** https://www.cloudera.com - **Year Founded:** 2008 - **HQ Location:** Santa Clara, CA - **Twitter:** @cloudera (106,442 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/229433/ (3,446 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Engineer, Software Engineer - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 42% Enterprise, 32% Small-Business #### What Are Cloudera's Pros and Cons? **Pros:** - Ease of Use (22 reviews) - Scalability (17 reviews) - Security (9 reviews) - Data Management (8 reviews) - Features (8 reviews) **Cons:** - Expensive (16 reviews) - Complexity (7 reviews) - Difficult Learning (5 reviews) - Poor Documentation (4 reviews) - Access Issues (3 reviews) ### What Do G2 Reviewers Say About Cloudera? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **ease of use** of Cloudera, highlighting its intuitive interface for managing big data efficiently. - Users appreciate the **easy scalability** of Cloudera, praising its capability to manage large data efficiently. - Users highlight the **strong security features** of Cloudera, ensuring reliable management of sensitive data. - Users value the **comprehensive suite of tools** in Cloudera for effective big data management and analytics. - Users value the **scalability and ease of use** in Cloudera, enhancing data processing and reporting efficiency. **Cons:** - Users express that Cloudera can be quite **expensive** , especially in terms of setup and ongoing maintenance costs. - Users find Cloudera's **complexity** in SQL queries challenging, especially for those lacking experience and resources. - Users find Cloudera challenging to learn, noting **difficult learning** curves and the need for better tutorials. - Users find **poor documentation** a major hurdle, complicating navigation and setup of Cloudera's data processing features. - Users experience **access issues** with Cloudera, particularly regarding unauthorized errors and limited documentation support. #### What Are Recent G2 Reviews of Cloudera? **"[Reliable Platform for Managing Large-Scale Data Pipelines](https://www.g2.com/survey_responses/cloudera-review-11455117)"** **Rating:** 4.5/5.0 stars *— Paritosh C.* [Read full review](https://www.g2.com/survey_responses/cloudera-review-11455117) --- **"[Easy to Use, Reliable, and Great for Team Collaboration](https://www.g2.com/survey_responses/cloudera-review-12695378)"** **Rating:** 4.0/5.0 stars *— Verified User in Computer Software* [Read full review](https://www.g2.com/survey_responses/cloudera-review-12695378) --- #### What Are G2 Users Discussing About Cloudera? - [What is Cloudera used for?](https://www.g2.com/discussions/what-is-cloudera-used-for) - 1 comment - [What is Hortonworks Data Platform used for?](https://www.g2.com/discussions/what-is-hortonworks-data-platform-used-for) - [What is Cloudera Data Flow used for?](https://www.g2.com/discussions/what-is-cloudera-data-flow-used-for) - [What is Cloudera Navigator used for?](https://www.g2.com/discussions/what-is-cloudera-navigator-used-for) - [What is Cloudera Data Engineering used for?](https://www.g2.com/discussions/what-is-cloudera-data-engineering-used-for) ### 21. [Azure Machine Learning](https://www.g2.com/products/microsoft-azure-machine-learning/reviews) Azure Machine Learning is an enterprise-grade service that facilitates the end-to-end machine learning lifecycle, enabling data scientists and developers to build, train, and deploy models efficiently. Key Features and Functionality: - Data Preparation: Quickly iterate data preparation on Apache Spark clusters within Azure Machine Learning, interoperable with Microsoft Fabric. - Feature Store: Increase agility in shipping your models by making features discoverable and reusable across workspaces. - AI Infrastructure: Take advantage of purpose-built AI infrastructure uniquely designed to combine the latest GPUs and InfiniBand networking. - Automated Machine Learning: Rapidly create accurate machine learning models for tasks including classification, regression, vision, and natural language processing. - Responsible AI: Build responsible AI solutions with interpretability capabilities. Assess model fairness through disparity metrics and mitigate unfairness. - Model Catalog: Discover, fine-tune, and deploy foundation models from Microsoft, OpenAI, Hugging Face, Meta, Cohere, and more using the model catalog. - Prompt Flow: Design, construct, evaluate, and deploy language model workflows with prompt flow. - Managed Endpoints: Operationalize model deployment and scoring, log metrics, and perform safe model rollouts. Primary Value and Solutions Provided: Azure Machine Learning accelerates time to value by streamlining prompt engineering and machine learning model workflows, facilitating faster model development with powerful AI infrastructure. It streamlines operations by enabling reproducible end-to-end pipelines and automating workflows with continuous integration and continuous delivery (CI/CD). The platform ensures confidence in development through unified data and AI governance with built-in security and compliance, allowing compute to run anywhere for hybrid machine learning. Additionally, it promotes responsible AI by providing visibility into models, evaluating language model workflows, and mitigating fairness, biases, and harm with built-in safety systems. **Average Rating:** 4.3/5.0 **Total Reviews:** 87 **How Do G2 Users Rate Azure Machine Learning?** - **Application:** 8.8/10 (Category avg: 8.5/10) - **Managed Service:** 8.9/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.7/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Azure Machine Learning?** - **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft) - **Year Founded:** 1975 - **HQ Location:** Redmond, Washington - **Twitter:** @microsoft (13,091,739 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/microsoft/ (231,632 employees on LinkedIn®) - **Ownership:** MSFT **Who Uses This Product?** - **Who Uses This:** Software Engineer - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 40% Enterprise, 33% Small-Business #### What Are Azure Machine Learning's Pros and Cons? **Pros:** - Ease of Use (3 reviews) - Features (3 reviews) - Customer Support (2 reviews) - Data Management (2 reviews) - Efficiency (2 reviews) **Cons:** - Learning Curve (3 reviews) - Difficult Navigation (2 reviews) - UX Improvement (2 reviews) - Complex Interface (1 reviews) - Difficult Learning (1 reviews) ### What Do G2 Reviewers Say About Azure Machine Learning? *AI-generated summary from verified user reviews* **Pros:** - Users find Azure Machine Learning to be **easy to use** , with helpful resources and an intuitive interface for implementation. - Users value the **scalability and integration** of Azure Machine Learning, enhancing their machine learning projects effectively. - Users find the **customer support for Azure Machine Learning** exceptional, aided by thorough documentation and community help. - Users appreciate the **easy-to-use data management features** in Azure Machine Learning, aiding effective pattern recognition and organization. - Users value the **efficiency** of Azure Machine Learning, enjoying its intuitive UI and seamless job management. **Cons:** - Users struggle with the **steep learning curve** of Azure Machine Learning, making initial usage challenging and time-consuming. - Users find **difficult navigation** in Azure Machine Learning due to a disordered interface and non-intuitive flows. - Users find the **disordered user interface** of Azure Machine Learning complicates navigation and usability significantly. - Users find Azure Machine Learning's **complex interface** non-intuitive, particularly with job management and navigation challenges. - Users face a challenging **learning curve** with Azure Machine Learning, making it hard for newcomers to adapt effectively. #### What Are Recent G2 Reviews of Azure Machine Learning? **"[An Enterprise-Grade Way to Operationalize ML](https://www.g2.com/survey_responses/azure-machine-learning-review-12853548)"** **Rating:** 4.0/5.0 stars *— Vytas J.* [Read full review](https://www.g2.com/survey_responses/azure-machine-learning-review-12853548) --- **"[Cost-Efficient Medical Data Integration Backed by Great Support](https://www.g2.com/survey_responses/azure-machine-learning-review-12845990)"** **Rating:** 5.0/5.0 stars *— Giridharan U.* [Read full review](https://www.g2.com/survey_responses/azure-machine-learning-review-12845990) --- #### What Are G2 Users Discussing About Azure Machine Learning? - [What is Azure Machine Learning Studio used for?](https://www.g2.com/discussions/what-is-azure-machine-learning-studio-used-for) - 1 comment - [What type of data analysis is azure machine learning studio intended for?](https://www.g2.com/discussions/what-type-of-data-analysis-is-azure-machine-learning-studio-intended-for) - [What are the key features of Azure Machine Learning?](https://www.g2.com/discussions/what-are-the-key-features-of-azure-machine-learning) - [How do I use Microsoft Azure for machine learning?](https://www.g2.com/discussions/how-do-i-use-microsoft-azure-for-machine-learning) - [What is Azure Machine Learning Studio?](https://www.g2.com/discussions/what-is-azure-machine-learning-studio) ### 22. [Pecan](https://www.g2.com/products/pecan/reviews) Pecan AI is a predictive analytics platform that helps business teams understand what’s likely to happen next, while there is still time to act. With Pecan’s Predictive AI Agent, teams can turn business questions into reliable predictions for use cases like customer churn, demand forecasting, and lifetime value, without relying on long, complex data science projects. The platform automatically handles data preparation, feature engineering, modeling, validation, and delivery, and provides transparent, explainable predictions that integrate into tools like Salesforce, HubSpot, Snowflake, and BI systems to drive real business outcomes. **Average Rating:** 4.7/5.0 **Total Reviews:** 36 **How Do G2 Users Rate Pecan?** - **Application:** 8.3/10 (Category avg: 8.5/10) - **Managed Service:** 8.3/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 5.0/10 (Category avg: 8.2/10) - **Ease of Admin:** 7.9/10 (Category avg: 8.5/10) **Who Is the Company Behind Pecan?** - **Seller:** [Pecan.ai](https://www.g2.com/sellers/pecan-ai) - **Company Website:** https://www.pecan.ai - **Year Founded:** 2018 - **HQ Location:** US, Israel - **Twitter:** @pecan_ai (1,135 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/pecan-ai/ (89 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Retail - **Company Size:** 54% Mid-Market, 21% Enterprise #### What Are Pecan's Pros and Cons? **Pros:** - Ease of Use (25 reviews) - Customer Support (18 reviews) - Speed (15 reviews) - Problem Solving (13 reviews) - Implementation Ease (11 reviews) **Cons:** - Learning Difficulty (9 reviews) - Limitations (8 reviews) - Limited Features (8 reviews) - Learning Curve (7 reviews) - Limited Customization (5 reviews) ### What Do G2 Reviewers Say About Pecan? *AI-generated summary from verified user reviews* **Pros:** - Users value the **ease of use** of Pecan, noting its simplicity and flexibility for effective predictions. - Users commend Pecan's **excellent customer support** , always available to enhance product use and ensure success. - Users value the **speed and simplicity** of Pecan, facilitating quick setup and efficient modeling for their business needs. - Users find Pecan's **exceptional support** and tools effective for solving business problems and enhancing model implementation. - Users appreciate the **implementation ease** of Pecan, enjoying quick setups and excellent support throughout the process. **Cons:** - Users note a significant **learning difficulty** with Pecan, especially in understanding data and navigating its functionalities. - Users experience **limitations in model selection and customization** , hindering their control over specific use cases. - Users feel limited by the **restricted features** of Pecan, lacking deeper control over model selection and customization. - Users note a **steep learning curve** with Pecan, particularly in understanding data and navigating setup challenges. - Users desire **greater customization options** for models in Pecan, seeking more control over tuning and selection. #### What Are Recent G2 Reviews of Pecan? **"[AI Chatbox Integration Makes Feature Development Easy to Explore and Iterate](https://www.g2.com/survey_responses/pecan-review-12878894)"** **Rating:** 4.0/5.0 stars *— Yuqi L.* [Read full review](https://www.g2.com/survey_responses/pecan-review-12878894) --- **"[Intuitive Platform with Exceptional Support](https://www.g2.com/survey_responses/pecan-review-12654479)"** **Rating:** 5.0/5.0 stars *— J G.* [Read full review](https://www.g2.com/survey_responses/pecan-review-12654479) --- ### 23. [Google Cloud AutoML](https://www.g2.com/products/google-cloud-automl/reviews) Google Cloud AutoML is a suite of machine learning products designed to enable developers with limited expertise to train high-quality custom models tailored to their specific business needs. By leveraging Google's advanced transfer learning and neural architecture search technologies, AutoML simplifies the process of building, deploying, and scaling machine learning models, making AI more accessible to a broader audience. Key Features and Functionality: - Automated Model Training: AutoML automates the selection of model architecture and hyperparameter tuning, reducing the need for manual intervention and specialized knowledge. - User-Friendly Interface: The platform offers an intuitive graphical interface that allows users to upload data, train models, and manage deployments with ease. - Versatile Model Types: AutoML supports various data types and tasks through specialized services: - AutoML Vision: For image classification and object detection. - AutoML Natural Language: For text classification, sentiment analysis, and entity recognition. - AutoML Translation: For creating custom translation models between language pairs. - AutoML Video Intelligence: For video classification and object tracking. - AutoML Tables: For structured data tasks like regression and classification. - Seamless Integration: AutoML integrates with other Google Cloud services, facilitating efficient data management, model deployment, and scalability. Primary Value and Problem Solving: Google Cloud AutoML democratizes machine learning by enabling users without deep technical expertise to develop and deploy custom models. This accessibility allows businesses to harness the power of AI to solve complex problems, such as improving customer experiences through personalized recommendations, automating content moderation, enhancing language translation services, and gaining insights from large datasets. By reducing the barriers to entry, AutoML empowers organizations to innovate and stay competitive in their respective industries. **Average Rating:** 4.1/5.0 **Total Reviews:** 23 **How Do G2 Users Rate Google Cloud AutoML?** - **Natural Language Understanding:** 8.3/10 (Category avg: 8.2/10) - **Ease of Admin:** 7.9/10 (Category avg: 8.5/10) **Who Is the Company Behind Google Cloud AutoML?** - **Seller:** [Google](https://www.g2.com/sellers/google) - **Year Founded:** 1998 - **HQ Location:** Mountain View, CA - **Twitter:** @google (31,899,995 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (341,888 employees on LinkedIn®) - **Ownership:** NASDAQ:GOOG **Who Uses This Product?** - **Company Size:** 42% Small-Business, 29% Enterprise #### What Are Google Cloud AutoML's Pros and Cons? **Pros:** - AI Integration (1 reviews) - Ease of Use (1 reviews) - Easy Integrations (1 reviews) - Integrated Platform (1 reviews) - Intuitive (1 reviews) **Cons:** - Cost (1 reviews) - Expensive (1 reviews) ### What Do G2 Reviewers Say About Google Cloud AutoML? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **seamless AI integration** of Google Cloud AutoML, simplifying high-quality model training for everyone. - Users find Google Cloud AutoML's **intuitive interface** and seamless integration greatly enhances their machine learning experience. - Users value the **easy integrations** of Google Cloud AutoML, enjoying its seamless connection with other Google Cloud services. - Users value the **seamless integration** of Google Cloud AutoML with other Google Cloud services, enhancing their overall experience. - Users value the **intuitive interface** of Google Cloud AutoML, facilitating easier machine learning model training for all skill levels. **Cons:** - Users find the **cost prohibitive** for small projects or students, impacting accessibility and budget considerations. - Users find the **pricing expensive** , making it challenging for small projects and students to utilize Google Cloud AutoML. #### What Are Recent G2 Reviews of Google Cloud AutoML? **"[Faster ML Model Development by Google](https://www.g2.com/survey_responses/google-cloud-automl-review-13088393)"** **Rating:** 4.0/5.0 stars *— Verified User in Information Technology and Services* [Read full review](https://www.g2.com/survey_responses/google-cloud-automl-review-13088393) --- **"[Easy to Use Yet Customizable, with Room to Grow into Vertex AI](https://www.g2.com/survey_responses/google-cloud-automl-review-13076121)"** **Rating:** 4.5/5.0 stars *— Nikhil M.* [Read full review](https://www.g2.com/survey_responses/google-cloud-automl-review-13076121) --- #### What Are G2 Users Discussing About Google Cloud AutoML? - [What is Google Cloud AutoML used for?](https://www.g2.com/discussions/what-is-google-cloud-automl-used-for) ### 24. [Altair AI Studio](https://www.g2.com/products/rapidminer-studio/reviews) Altair AI Studio (formerly RapidMiner Studio) is a data science tool that anyone can use to design and prototype highly explainable AI and machine learning models that help build trust throughout an organization. Altair AI Studio includes: - Full generative AI functionality with access to hundreds of large language models (LLMs). - Intuitive and powerful drag-and-drop canvases that give users code-like control without complexity. - Award-winning auto ML with automated clustering, predictive modeling, feature engineering, and time series forecasting. - Data connectivity, exploration, and preparation. - Deploy and manage AI projects and models at enterprise scale. - Collaborate with team members in the same environment without having to worry about overwriting each other's work. - Unify the entire data science lifecycle from data exploration and machine learning to model operations and visualization and deploy in the cloud. Altair AI Studio helps users make powerful insights accessible to the entire organization and can scale seamlessly for users and enterprises. Altair AI studio enables organizations to derive significant value from AI with minimal cost and operational impact. **Average Rating:** 4.6/5.0 **Total Reviews:** 494 **How Do G2 Users Rate Altair AI Studio?** - **Application:** 8.3/10 (Category avg: 8.5/10) - **Managed Service:** 8.2/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 7.6/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.4/10 (Category avg: 8.5/10) **Who Is the Company Behind Altair AI Studio?** - **Seller:** [Altair](https://www.g2.com/sellers/altair-186799f5-3238-493f-b3ad-b8cac484afd7) - **Company Website:** https://www.altair.com/ - **Year Founded:** 1985 - **HQ Location:** Troy, MI - **LinkedIn® Page:** https://www.linkedin.com/company/8323/ (2,774 employees on LinkedIn®) - **Ownership:** NASDAQ:ALTR **Who Uses This Product?** - **Who Uses This:** Student, Data Scientist - **Top Industries:** Higher Education, Education Management - **Company Size:** 42% Small-Business, 30% Mid-Market #### What Are Altair AI Studio's Pros and Cons? **Pros:** - Ease of Use (9 reviews) - Machine Learning (8 reviews) - AI Integration (6 reviews) - AI Technology (5 reviews) - Automation (5 reviews) **Cons:** - Complexity (4 reviews) - Large Dataset Handling (3 reviews) - Slow Performance (3 reviews) - Complexity Issues (2 reviews) - Complex Usage (2 reviews) ### What Do G2 Reviewers Say About Altair AI Studio? *AI-generated summary from verified user reviews* **Pros:** - Users enjoy the **easy-to-use interface** of Altair AI Studio, allowing for seamless data science task completion. - Users value the **no-code machine learning capability** of Altair AI Studio, facilitating easy model creation and advanced analytics. - Users appreciate the **seamless AI integration** of Altair AI Studio, enhancing decision-making and efficiency in data analysis. - Users admire the **advanced machine learning and data analytics** in Altair AI Studio, enhancing decision-making and efficiency. - Users value the **automation capabilities** of Altair AI Studio, significantly enhancing efficiency and decision-making processes. **Cons:** - Users find the **complexity** of Altair AI Studio challenging, especially with language support and handling large datasets. - Users face **slower performance** and occasional UI bugs when handling large datasets in Altair AI Studio. - Users face **slow performance** with Altair AI Studio, particularly when managing large or complex datasets. - Users find the **complexity issues** of Altair AI Studio frustrating, especially due to limited support options. - Users find the **complex usage** of Altair AI Studio challenging due to steep learning curves and inadequate documentation. #### What Are Recent G2 Reviews of Altair AI Studio? **"[Essential Tool for Streamlined Sensor Analysis](https://www.g2.com/survey_responses/altair-ai-studio-review-12568188)"** **Rating:** 5.0/5.0 stars *— Ayçe M.* [Read full review](https://www.g2.com/survey_responses/altair-ai-studio-review-12568188) --- **"[Great tool for easy data analysis and testing of AI models](https://www.g2.com/survey_responses/altair-ai-studio-review-12942088)"** **Rating:** 4.5/5.0 stars *— Sanjeet S.* [Read full review](https://www.g2.com/survey_responses/altair-ai-studio-review-12942088) --- #### What Are G2 Users Discussing About Altair AI Studio? - [What is RapidMiner used for?](https://www.g2.com/discussions/what-is-rapidminer-used-for) - 1 comment - [What are the data mining tools?](https://www.g2.com/discussions/what-are-the-data-mining-tools) - [Is RapidMiner open source?](https://www.g2.com/discussions/is-rapidminer-open-source) - [How do you use the Rapid Miner?](https://www.g2.com/discussions/how-do-you-use-the-rapid-miner) - [Is Rapid Miner legit?](https://www.g2.com/discussions/is-rapid-miner-legit) ### 25. [IBM SPSS Modeler](https://www.g2.com/products/ibm-spss-modeler/reviews) The IBM SPSS Modeler is a leading, visual data science and machine learning solution. It helps enterprises accelerate time to value and desired outcome by speeding the operational tasks for data scientists. Leading organizations worldwide rely on IBM for data discovery, predictive analytics, model management and deployment, and machine learning to monetize data assets. The IBM SPSS Modeler empowers organizations to tap data assets and modern applications with complete, out-of-box algorithms and models, suited for hybrid, multi-cloud environments with robust governance and security posture. • Take advantage of open source based innovation including R or Python • Empower data scientists of all skills – programmatic and visual • Exploit multi-cloud approach - on-prem, public or private clouds • Start small and scale to enterprise-wide, governed approach **Average Rating:** 4.0/5.0 **Total Reviews:** 128 **How Do G2 Users Rate IBM SPSS Modeler?** - **Application:** 7.5/10 (Category avg: 8.5/10) - **Managed Service:** 7.6/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 6.4/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.1/10 (Category avg: 8.5/10) **Who Is the Company Behind IBM SPSS Modeler?** - **Seller:** [IBM](https://www.g2.com/sellers/ibm) - **Year Founded:** 1911 - **HQ Location:** Armonk, New York, United States - **Twitter:** @IBMSecurity (74,660 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (328,202 employees on LinkedIn®) - **Ownership:** SWX:IBM **Who Uses This Product?** - **Top Industries:** Higher Education, Education Management - **Company Size:** 53% Enterprise, 24% Mid-Market #### What Are IBM SPSS Modeler's Pros and Cons? **Pros:** - Analysis Capabilities (1 reviews) - Analytics (1 reviews) - Data Access (1 reviews) - Data Management (1 reviews) - Data Visualization (1 reviews) **Cons:** - Expensive (1 reviews) - Expensive Licensing (1 reviews) ### What Do G2 Reviewers Say About IBM SPSS Modeler? *AI-generated summary from verified user reviews* **Pros:** - Users value the **robust analytical modeling capabilities** of IBM SPSS Modeler for efficient data manipulation and integration. - Users appreciate the **seamless data connectivity** in IBM SPSS Modeler, enhancing their data manipulation and analysis experience. - Users appreciate the **seamless data access** in IBM SPSS Modeler, enhancing their workflow and data manipulation efficiency. - Users value the **seamless data manipulation** capabilities of IBM SPSS Modeler for efficient analytics and integration. - Users value the **seamless data visualization** capabilities of IBM SPSS Modeler, enhancing their data manipulation experience. **Cons:** - Users find the **licensing costs to be very high** , making the product expensive for each user. - Users find the **licensing costs to be excessively high** , making the product less accessible for many. #### What Are Recent G2 Reviews of IBM SPSS Modeler? **"[Seamless Data Integration and Powerful Analytics](https://www.g2.com/survey_responses/ibm-spss-modeler-review-12103509)"** **Rating:** 4.5/5.0 stars *— Verified User in Banking* [Read full review](https://www.g2.com/survey_responses/ibm-spss-modeler-review-12103509) --- **"[The best application for market research](https://www.g2.com/survey_responses/ibm-spss-modeler-review-8095256)"** **Rating:** 4.0/5.0 stars *— Basil E.* [Read full review](https://www.g2.com/survey_responses/ibm-spss-modeler-review-8095256) --- #### What Are G2 Users Discussing About IBM SPSS Modeler? - [What is IBM SPSS Modeler used for?](https://www.g2.com/discussions/ibm-spss-modeler-what-is-ibm-spss-modeler-used-for) - [What are the features of SPSS?](https://www.g2.com/discussions/ibm-spss-modeler-what-are-the-features-of-spss) - 1 comment - [What is IBM SPSS Modeler used for?](https://www.g2.com/discussions/what-is-ibm-spss-modeler-used-for) - 1 comment - [Is IBM SPSS Modeler free?](https://www.g2.com/discussions/is-ibm-spss-modeler-free) - 1 comment - [What is the difference between SPSS Statistics and Modeler?](https://www.g2.com/discussions/what-is-the-difference-between-spss-statistics-and-modeler) ## What Is Data Science and Machine Learning Platforms? [Artificial Intelligence Software](https://www.g2.com/categories/artificial-intelligence) ## What Software Categories Are Similar to Data Science and Machine Learning Platforms? - [Predictive Analytics Software](https://www.g2.com/categories/predictive-analytics) - [Analytics Platforms](https://www.g2.com/categories/analytics-platforms) - [MLOps Platforms](https://www.g2.com/categories/mlops-platforms) --- ## How Do You Choose the Right Data Science and Machine Learning Platforms? ### What You Should Know About Data Science and Machine Learning Platforms ### What are data science and machine learning (DSML) platforms? The amount of data being produced within companies is increasing rapidly. Businesses are realizing its importance and are leveraging this accumulated data to gain a competitive advantage. Companies are turning their data into insights to drive business decisions and improve product offerings. With data science, of which [artificial intelligence (AI)](https://www.g2.com/articles/what-is-artificial-intelligence) is a part, users can mine vast amounts of data. Whether structured or unstructured, it uncovers patterns and makes data-driven predictions. One crucial aspect of data science is the development of machine learning models. Users leverage data science and machine learning engineering platforms that facilitate the entire process, from data integration to model management. With this single platform, data scientists, engineers, developers, and other business stakeholders collaborate to ensure that the data is appropriately managed and mined for meaning. ### Types of DSML platforms Not all data science and machine learning software platforms are designed equal. These tools allow developers and data scientists to build, train, and deploy [machine learning models](https://www.g2.com/articles/what-is-machine-learning). However, they differ in terms of the data types supported and the method and manner of deployment.  **Cloud** **data science and machine learning platforms** With the ability to store data in remote servers and easily access it, businesses can focus less on building infrastructure and more on their data, both in terms of how to derive insight from it and to ensure its quality. Cloud-based DSML platforms afford them the ability to both train and deploy the models in the cloud. This also helps when these models are being built into various applications, as it provides easier access to change and tweak the models that have been deployed. **On-premises** **data science and machine learning platforms** Cloud is not always the answer, as it is not always a viable solution. Not all data experts have the luxury of working in the cloud for several reasons, including data security and issues related to latency. In cases like health care, strict regulations, such as [HIPAA](https://www.g2.com/glossary/hipaa-definition), require data to be secure. Therefore, on-premises DSML solutions can be vital for some professionals, such as those in the healthcare industry and government sector, where privacy compliance is stringent and sometimes necessary. **Edge** **platforms** Some DSML tools and software allow for spinning up algorithms on the edge, consisting of a mesh network of [data centers](https://www.g2.com/glossary/data-center-definition) that process and store data locally before being sent to a centralized storage center or cloud. [Edge computing](https://learn.g2.com/trends/edge-computing) optimizes cloud computing systems to avoid disruptions or slowing in the sending and receiving of data. ** ** ### What are the common features of data science and machine learning solutions? The following are some core features within data science and machine learning platforms that can help users prepare data and train, manage, and deploy models. **Data preparation:** Data ingestion features allow users to integrate and ingest data from various internal or external sources, such as enterprise applications, databases, or Internet of Things (IoT) devices. Dirty data (i.e., incomplete, inaccurate, or incoherent data) is a nonstarter for building machine learning models. Bad AI training begets bad models, which in turn begets bad predictions that may be useful at best and detrimental at worst. Therefore, data preparation capabilities allow for [data cleansing](https://www.g2.com/articles/data-cleaning) and data augmentation (in which related datasets are brought to bear on company data) to ensure that the data journey gets off to a good start. **Model training:** Feature engineering transforms raw data into features that better represent the underlying problem to the predictive models. It is a key step in building a model and improves model accuracy on unseen data. Building a model requires training it by feeding it data. Training a model is the process of determining the proper values for all the weights and the bias from the inputted data. Two key methods used for this purpose are [supervised learning and unsupervised learning](https://www.g2.com/articles/supervised-vs-unsupervised-learning). The former is a method in which the input is labeled, whereas the latter deals with unlabeled data. **Model management:** The process does not end once the model is released. Businesses must monitor and manage their models to ensure that they remain accurate and updated. Model comparison allows users to quickly compare models to a baseline or to a previous result to determine the quality of the model built. Many of these platforms also have tools for tracking metrics, such as accuracy and loss. **Model deployment:** The deployment of machine learning models is the process of making them available in production environments, where they provide predictions to other software systems. Methods of deployment include REST APIs, GUI for on-demand analysis, and more. ### What are the benefits of using DSML engineering platforms? Through the use of data science and machine learning platforms, data scientists can gain visibility into the entire data journey, from ingestion to inference. This helps them better understand what is and isn’t working and provides them with the tools necessary to fix problems if and when they arise. With these tools, experts prepare and enrich their data, leverage machine learning libraries, and deploy their algorithms into production. **Share data insights:** Users can share data, models, dashboards, or other related information with collaboration-based tools to foster and facilitate teamwork. **Simplify and scale data science:** Many platforms are opening up these tools to a broader audience with easy-to-use features and drag-and-drop capabilities. In addition, pre-trained models and out-of-the-box pipelines tailored to specific tasks help streamline the process. These platforms easily help scale up experiments across many nodes to perform distributed training on large datasets. **Experimentation:** Before a model is pushed to production, data scientists spend a significant amount of time working with the data and experimenting to find an optimal solution. Data science and machine learning vendors facilitate this experimentation through data visualization, data augmentation, and data preparation tools. Different types of layers and optimizers for [deep learning](https://www.g2.com/articles/deep-learning), which are algorithms or methods used to change the attributes of neural networks, such as weights and learning rate, to reduce losses, are also used in experimentation. ### Who uses data science and machine learning products? Data scientists are in high demand, but skilled professionals are in shortage. The skillset is varied and vast (for example, there is a need to understand various algorithms, advanced mathematics, programming skills, and more). Therefore, such professionals are difficult to come by and command high compensation. To tackle this issue, platforms increasingly include features that make it easier to develop AI solutions, such as drag-and-drop capabilities and prebuilt algorithms. In addition, for data science projects to initiate, it is key that the broader business buys into them. The more robust platforms provide resources that help nontechnical users understand the models, the data involved, and the aspects of the business that have been impacted. **Data engineers:** With robust data integration capabilities, data engineers tasked with the design, integration, and management of data use these platforms to collaborate with data scientists and other stakeholders within the organization. **Citizen data scientists:** With the rise of more user-friendly features, citizen data scientists, who are not professionally trained but have developed data skills, are increasingly turning to data science and machine learning platforms to bring AI into their organizations. **Professional data scientists:** Expert data scientists use these solutions to scale data science operations across the lifecycle, simplifying the process of experimentation to deployment and speeding up data exploration and preparation, as well as model development and training. **Business stakeholders:** Business stakeholders use these tools to gain clarity into the machine learning models and better understand how they tie in with the broader business and its operations. ### What are the alternatives to data science and machine learning platforms? Alternatives to data science and machine learning solutions can replace this type of software, either partially or completely: [AI & machine learning operationalization software](https://www.g2.com/categories/ai-machine-learning-operationalization) **:** Depending on the use case, businesses might consider AI and machine learning operationalization software. This software does not provide a platform for the full end-to-end development of machine learning models but can provide more robust features around operationalizing these algorithms. This includes monitoring the health, performance, and accuracy of models. [Machine learning software](https://www.g2.com/categories/machine-learning) **:** Data science and machine learning platforms are great for the full-scale development of models, whether that be for [computer vision](https://learn.g2.com/computer-vision), natural language processing (NLP), and more. However, in some cases, businesses may want a solution that is more readily available off the shelf, which they can use in a plug-and-play fashion. In such a case, they can consider machine learning software, which will involve less setup time and development costs. There are many different types of machine learning algorithms that perform a variety of tasks and functions. These algorithms may consist of more specific ones, such as association rule learning, [Bayesian networks](https://www.g2.com/articles/artificial-intelligence-terms#:~:text=Bayesian%20network%3A%20also%20known%20as%20the%20Bayes%20network%2C%20Bayes%20model%2C%20belief%20network%2C%20and%20decision%20network%2C%20is%20a%20graph%2Dbased%20model%20representing%20a%20set%20of%20variables%20and%20their%20dependencies.%C2%A0), clustering, decision tree learning, genetic algorithms, learning classifier systems, and support vector machines, among others. This helps organizations look for point solutions. ### **Software and services related to data science and machine learning engineering platforms** Related solutions that can be used together with DSML platforms include: [Data preparation software](https://www.g2.com/categories/data-preparation) **:** Data preparation software helps companies with their data management. These solutions allow users to discover, combine, clean, and enrich data for simple analysis. Although data science and machine learning platforms offer data preparation features, businesses might opt for a dedicated preparation tool. [Data warehouse software](https://www.g2.com/categories/data-warehouse) **:** Most companies have many disparate data sources, and to best integrate all their data, they implement a data warehouse. Data warehouses house data from multiple databases and business applications, which allows business intelligence and analytics tools to pull all company data from a single repository. This organization is critical to the quality of the data ingested by data science and machine learning platforms. [Data labeling software](https://www.g2.com/categories/data-labeling) **:** To achieve supervised learning off the ground, it is key to have labeled data. Putting in place a systematic, sustained labeling effort can be aided by data labeling software, which provides a toolset for businesses to turn unlabeled data into labeled data and build corresponding AI algorithms. [Natural language processing (NLP) software](https://www.g2.com/categories/natural-language-processing-nlp) **:** [NLP](https://www.g2.com/articles/natural-language-processing) allows applications to interact with human language using a deep learning algorithm. NLP algorithms input language and give a variety of outputs based on the learned task. NLP algorithms provide [voice recognition](https://www.g2.com/articles/voice-recognition) and [natural language generation (NLG)](https://www.g2.com/categories/natural-language-generation-nlg), which converts data into understandable human language. Some examples of NLP uses include [chatbots](https://www.g2.com/categories/chatbots), translation applications, and [social media monitoring tools](https://www.g2.com/categories/social-media-listening-tools) that scan social media networks for mentions. ### Challenges with DSML platforms Software solutions can come with their own set of challenges.  **Data requirements:** A great deal of data is required for most AI algorithms to learn what is needed. Users need to train machine learning algorithms using techniques such as reinforcement learning, supervised learning, and unsupervised learning to build a truly intelligent application. **Skill shortage:** There is also a shortage of people who understand how to build these algorithms and train them to perform the necessary actions. The common user cannot simply fire up AI software and have it solve all their problems. **Algorithmic bias:** Although the technology is efficient, it is not always effective and is marred by various types of biases in the training data, such as race or gender biases. For example, since many facial recognition algorithms are trained on datasets with primarily white male faces, others are more likely to be falsely identified by the systems. ### Which companies should buy DSML engineering platforms? The implementation of AI can have a positive impact on businesses across a host of different industries. Here are a handful of examples: **Financial services:** AI is widely used in financial services, with banks using it for everything from developing credit score algorithms to analyzing earnings documents to spot trends. With data science and machine learning software solutions, data science teams can build models with company data and deploy them to internal and external applications. **Healthcare:** Within healthcare, businesses can use these platforms to better understand patient populations, such as predicting in-patient visits and developing systems that can match people with relevant clinical trials. In addition, as the process of drug discovery is particularly costly and takes a significant amount of time, healthcare organizations are using data science to speed up the process, using data from past trials, research papers, and more. **Retail:** In retail, especially e-commerce, personalization rules supreme. The top retailers are leveraging these platforms to provide customers with highly personalized experiences based on factors such as previous behavior and location. With machine learning in place, these businesses can display highly relevant material and catch the attention of potential customers.  ### How to choose the best data science and machine learning (DSML) platform #### Requirements gathering (RFI/RFP) for DSML platforms If a company is just starting out and looking to purchase its first data science and machine learning platform, or wherever a business is in its buying process, g2.com can help select the best option. The first step in the buying process must involve a careful look at one’s company data. As a fundamental part of the data science journey involves data engineering (i.e., data collection and analysis), businesses must ensure that their data quality is high and the platform in question can adequately handle their data, both in terms of format as well as volume. If the company has amassed a lot of data, it needs to look for a solution that can grow with the organization. Users should think about the pain points and jot them down; these should be used to help create a checklist of criteria. Additionally, the buyer must determine the number of employees who will need to use this software, as this drives the number of licenses they are likely to buy. Taking a holistic overview of the business and identifying pain points can help the team springboard into creating a checklist of criteria. The checklist serves as a detailed guide that includes both necessary and nice-to-have features, including budget, features, number of users, integrations, security requirements, cloud or on-premises solutions, and more. Depending on the deployment scope, producing an RFI, a one-page list with a few bullet points describing what is needed from a data science platform might be helpful. #### Compare DSML products **Create a long list** From meeting the business functionality needs to implementation, vendor evaluations are an essential part of the software buying process. For ease of comparison, after all demos are complete, it helps to prepare a consistent list of questions regarding specific needs and concerns to ask each vendor. **Create a short list** From the long list of vendors, it is helpful to narrow down the list of vendors and come up with a shorter list of contenders, preferably no more than three to five. With this list in hand, businesses can produce a matrix to compare the features and pricing of the various solutions. **Conduct demos** To ensure a thorough comparison, the user should demo each solution on the short list using the same use case and datasets. This will allow the business to evaluate like-for-like and see how each vendor compares against the competition. #### Selection of DSML platforms **Choose a selection team** Before getting started, it's crucial to create a winning team that will work together throughout the entire process, from identifying pain points to implementation. The software selection team should consist of members of the organization who have the right interests, skills, and time to participate in this process. A good starting point is to aim for three to five people who fill roles such as the main decision maker, project manager, process owner, system owner, or staffing subject matter expert, as well as a technical lead, IT administrator, or security administrator. In smaller companies, the vendor selection team may be smaller, with fewer participants, multitasking, and taking on more responsibilities. **Negotiation** Just because something is written on a company’s pricing page does not mean it is fixed (although some companies will not budge). It is imperative to open up a conversation regarding pricing and licensing. For example, the vendor may be willing to give a discount for multi-year contracts or to recommend the product to others. **Final decision** After this stage, and before going all in, it is recommended to roll out a test run or pilot program to test adoption with a small sample size of users. If the tool is well used and well received, the buyer can be confident that the selection was correct. If not, it might be time to go back to the drawing board. ### Cost of data science and machine learning platforms As mentioned above, data science and machine learning platforms are available as both on-premises and cloud solutions. Pricing between the two might differ, with the former often requiring more upfront infrastructure costs.  As with any software, these platforms are frequently available in different tiers, with the more entry-level solutions costing less than the enterprise-scale ones. The former will frequently not have as many features and may have usage caps. DSML vendors may have tiered pricing, in which the price is tailored to the users’ company size, the number of users, or both. This pricing strategy may come with some degree of support, which might be unlimited or capped at a certain number of hours per billing cycle. Once set up, they do not often require significant maintenance costs, especially if deployed in the cloud. As these platforms often come with many additional features, businesses looking to maximize the value of their software can contract third-party consultants to help them derive insights from their data and get the most out of the software. #### Return on Investment (ROI) Businesses decide to deploy data science and machine learning platforms with the goal of deriving some degree of ROI. As they are looking to recoup the losses that they spent on the software, it is critical to understand the costs associated with it. As mentioned above, these platforms typically are billed per user, which is sometimes tiered depending on the company size. More users will typically translate into more licenses, which means more money. Users must consider how much is spent and compare that to what is gained, both in terms of efficiency as well as revenue. Therefore, businesses can compare processes between pre- and post-deployment of the software to better understand how processes have been improved and how much time has been saved. They can even produce a case study (either for internal or external purposes) to demonstrate the gains they have seen from their use of the platform. ### Implementation of data science and machine learning platforms **How are DSML software tools implemented?** Implementation differs drastically depending on the complexity and scale of the data. In organizations with vast amounts of data in disparate sources (e.g., applications, databases, etc.), it is often wise to utilize an external party, whether that be an implementation specialist from the vendor or a third-party consultancy. With vast experience under their belts, they can help businesses understand how to connect and consolidate their data sources and how to use the software efficiently and effectively. **Who is responsible for DSML platform implementation?** It may require many people or teams to properly deploy a data science platform, including data engineers, data scientists, and software engineers. This is because, as mentioned, data can cut across teams and functions. As a result, one person or even one team rarely has a full understanding of all of a company’s data assets. With a cross-functional team in place, a business can begin to piece together its data and begin the journey of data science, starting with proper data preparation and management. **What is the implementation process for data science and machine learning products?** In terms of implementation, it is typical for the platform to be deployed in a limited fashion and subsequently rolled out in a broader fashion. For example, a retail brand might decide to A/B test its use of a personalization algorithm for a limited number of visitors to its site to understand better how it is performing. If the deployment is successful, the data science team can present their findings to their leadership team (which might be the CTO, depending on the structure of the business). If the deployment is unsuccessful, the team can return to the drawing board to determine what went wrong. This will involve examining the training data and algorithms used. If they try again, yet nothing seems to be successful (i.e., the outcome is faulty or there is no improvement in predictions), the business might need to go back to basics and review their data. **When should you implement DSML tools?** As previously mentioned, data engineering, which involves preparing and gathering data, is a fundamental feature of data science projects. Therefore, businesses must make getting their data in order their top priority, ensuring that there are no duplicate records or misaligned fields. Although this sounds basic, it is anything but. Faulty data as an input will result in faulty data as an output.  ### Data science and machine learning platforms trends **AutoML** AutoML helps automate many tasks needed to develop AI and machine learning applications. Uses include automatic data preparation, automated feature engineering, providing explainability for models, and more. **Embedded AI** Machine and deep learning functionality is getting increasingly embedded in nearly all types of software, irrespective of whether the user is aware of it. Using embedded AI inside software like [CRM](https://www.g2.com/categories/crm), [marketing automation](https://www.g2.com/categories/marketing-automation), and [analytics solutions](https://www.g2.com/categories/analytics-tools-software) allows us to streamline processes, automate certain tasks, and gain a competitive edge with predictive capabilities. Embedded AI may gradually pick up in the coming years and may do so in the same way cloud deployment and mobile capabilities have over the past decade. Eventually, vendors may not need to highlight their product benefits from machine learning as it may just be assumed and expected. **Machine learning as a service (MLaaS)** The software environment has moved to a more granular microservices structure, particularly for development operations needs. Additionally, the boom of public cloud infrastructure services has allowed large companies to offer development and infrastructure services to other businesses with a pay-as-you-use model. AI software is no different, as the same companies provide [MLaaS](https://www.g2.com/articles/machine-learning-as-a-service) for other enterprises. Developers quickly take advantage of these prebuilt algorithms and solutions by feeding them their data to gain insights. Using systems built by enterprise companies helps small businesses save time, resources, and money by eliminating the need to hire skilled machine learning developers. MLaaS will grow further as companies continue to rely on these microservices and the need for AI increases. **Explainability** When it comes to machine learning algorithms, especially deep learning, it may be difficult to explain how they arrived at certain conclusions. Explainable AI, also known as XAI, is the process whereby the decision-making process of algorithms is made transparent and understandable to humans. Transparency is the most prevalent principle in the current AI ethics literature, and hence explainability, a subset of transparency, becomes crucial. Data science and machine learning platforms are increasingly including tools for explainability, which helps users build explainability into their models and help them meet data explainability requirements in legislation such as the European Union's privacy law and the GDPR. --- ## What Are the Most Common Questions About Data Science and Machine Learning Platforms? *AI-generated · Last updated: April 27, 2026* ### Leading machine learning services for enterprise Based on G2 reviews, enterprise teams often favor platforms that unify data preparation, model training, deployment, governance, and monitoring in one environment. - [Vertex AI](https://www.g2.com/products/google-vertex-ai/reviews) — unified ML lifecycle and deployment. - [Databricks](https://www.g2.com/products/databricks/reviews) — lakehouse workflows with collaborative notebooks. - [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews) — large-scale analytics with governance. - [IBM watsonx.ai](https://www.g2.com/products/ibm-watsonx-ai/reviews) — governed AI development for enterprises. ### Top-rated software for data analysis in SaaS industry Based on G2 reviews, buyers in software environments often prioritize platforms that shorten analysis cycles, support collaboration, and reduce tool switching. - [Hex](https://www.g2.com/products/hex-tech-hex/reviews) — SQL, Python, and dashboarding together. - [Vertex AI](https://www.g2.com/products/google-vertex-ai/reviews) — end-to-end ML workflows in one place. - [Databricks](https://www.g2.com/products/databricks/reviews) — scalable analytics and ML collaboration. - [Deepnote](https://www.g2.com/products/deepnote/reviews) — collaborative notebooks for team analysis. ### Which platform offers the best machine learning solutions Based on G2 reviews, the strongest options depend on whether your team values unified workflows, low-code model building, notebook collaboration, or governance. - [Vertex AI](https://www.g2.com/products/google-vertex-ai/reviews) — managed training, deployment, and monitoring. - [Databricks](https://www.g2.com/products/databricks/reviews) — engineering, analytics, and ML together. - [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews) — advanced analytics with strong controls. - [Anaconda Platform](https://www.g2.com/products/anaconda-platform/reviews) — reproducible environments and package management. ### What are data science and machine learning platforms used for According to verified users, data science and machine learning platforms are used to centralize the work of preparing data, building models, testing ideas, deploying models, and sharing results. Reviews repeatedly mention workflow simplification as a major benefit: teams can reduce tool switching, automate repetitive preparation tasks, and move from experimentation to production with less manual setup. Buyers also use these platforms for dashboards, forecasting, predictive modeling, model monitoring, collaboration across technical and non-technical teams, and connecting data from warehouses, cloud systems, spreadsheets, or operational tools. Common buyer concerns in the reviews include learning curve, documentation quality, cost visibility, and performance on very large workloads. ### How do teams use data science and machine learning platforms for collaboration According to verified users, collaboration is one of the most practical reasons teams adopt these platforms. Reviews describe analysts, data scientists, and engineers working in shared notebooks, common environments, and governed workspaces so they can move from raw data to analysis, visualizations, and deployed models without passing files back and forth. Teams also mention easier sharing of dashboards, published apps, reusable workflows, and reproducible environments. In several reviews, this reduces friction between technical and non-technical stakeholders because results can be reviewed, discussed, and reused in one place. The strongest collaboration themes in the recent reviews are shared notebooks, consistent environments, versioned workflows, and easier handoffs into production.