# 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 (1,284 reviews) | Unified lakehouse ML and analytics workflows | "[Great Spark Scaling, But Slow Cluster Boot Times](https://www.g2.com/survey_responses/databricks-review-12905667)" | | 2 | [Gemini Enterprise Agent Platform](https://www.g2.com/products/gemini-enterprise-agent-platform/reviews) | 4.3/5.0 (652 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 (758 reviews) | End-to-end ML lifecycle with governed model deployment | "[Powerful & Transforming Data into Decisions—Effortlessly and Intelligently.](https://www.g2.com/survey_responses/sas-viya-review-12682824)" | | 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 | [Deepnote](https://www.g2.com/products/deepnote/reviews) | 4.5/5.0 (377 reviews) | Collaborative notebook analytics with multi-source integration | "[Clarity for complex nutrition work](https://www.g2.com/survey_responses/deepnote-review-12699174)" | | 6 | [Dataiku](https://www.g2.com/products/dataiku/reviews) | 4.4/5.0 (204 reviews) | End-to-end ML workflows with no-code/code flexibility | "[VisualML Potente con Limitaciones en Procesamiento Masivo](https://www.g2.com/survey_responses/dataiku-review-12982887)" | | 7 | [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)" | | 8 | [Hex](https://www.g2.com/products/hex-tech-hex/reviews) | 4.5/5.0 (399 reviews) | Polyglot SQL-Python notebooks with AI-assisted analysis | "[Effortless Data Analysis with Powerful AI](https://www.g2.com/survey_responses/hex-review-12262172)" | | 9 | [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)" | | 10 | [MATLAB](https://www.g2.com/products/matlab/reviews) | 4.5/5.0 (749 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)" | ## How Many Data Science and Machine Learning Platforms Products Does G2 Track? **Total Products under this Category:** 891 ### Category Stats (Jun 2026) - **Average Rating**: 4.45/5 The average rating of products in this category, based on all submitted ratings - **Top Trending Product**: KNIME (+1.04%) - Among all products in this category, KNIME recorded the largest rating increase compared to last month *Last updated: June 24, 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 - 13,800+ Authentic Reviews - 891+ 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** ### SAS Viya 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. [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%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=1327283&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=4dbaceab5366df37176b9e77a4e8caea60b9a65e6b92269a2c6ea621c85a3462&secure%5Burl%5D=https%3A%2F%2Fwww.sas.com%2Fgms%2Fredirect.jsp%3Fdetail%3DPLN73455_275629423&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:** 1,284 **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 (288 reviews) - Ease of Use (278 reviews) - Integrations (189 reviews) - Collaboration (150 reviews) - Data Management (150 reviews) **Cons:** - Learning Curve (112 reviews) - Expensive (97 reviews) - Steep Learning Curve (96 reviews) - Missing Features (69 reviews) - Complexity (64 reviews) ### What Do G2 Reviewers Say About Databricks? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **seamless integration** of Databricks with AWS and its powerful features for data management and security. - Users find Databricks to offer **exceptional ease of use** , making data integration and management seamless and efficient. - Users praise the **seamless integration** of Databricks with AWS and Azure, enhancing collaboration and efficiency in data management. - Users appreciate the **excellent collaboration** in Databricks, facilitating real-time teamwork for data engineers and analysts. - Users value the **effective data management features** of Databricks, enhancing usability and decision-making with integrated tools. **Cons:** - Users face a **steep learning curve** with Databricks, complicating initial adoption and resource management. - Users find the **costs to be high** for Databricks, especially when dealing with large data sets. - Users face a **steep learning curve** with Databricks, making initial adoption challenging and requiring specialized support. - Users are disappointed by the **missing features** in Databricks, limiting customization and complicating development processes. - Users find the **complexity** of Databricks challenging due to steep learning curves and integration limitations. #### What Are Recent G2 Reviews of Databricks? **"[Great Spark Scaling, But Slow Cluster Boot Times](https://www.g2.com/survey_responses/databricks-review-12905667)"** **Rating:** 5.0/5.0 stars *— Raj P.* [Read full review](https://www.g2.com/survey_responses/databricks-review-12905667) --- **"[Powerful Lakehouse for Big Data, Collaboration, and Efficient Pipelines](https://www.g2.com/survey_responses/databricks-review-12946286)"** **Rating:** 4.5/5.0 stars *— Konjengbam M.* [Read full review](https://www.g2.com/survey_responses/databricks-review-12946286) --- #### 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:** 652 **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 appreciate the **ease of use** of Gemini Enterprise Agent Platform, enhancing productivity with its streamlined setup and functionality. - Users value the **multimodal capabilities** of Gemini, enhancing productivity and streamlining machine learning workflows efficiently. - Users value the **multimodal capabilities** of Gemini, enhancing productivity by integrating text, images, code, and documents. - Users value the **model variety** of Gemini, enhancing productivity through its comprehensive multimodal capabilities. - Users value the **integrated platform** of Gemini for its seamless handling of diverse tasks in one solution. **Cons:** - Users find the **pricing tricky** to estimate for Gemini Enterprise Agent Platform, leading to potential unexpected bills. - Users find the **complexity of Vertex AI** overwhelming, particularly with IAM roles and navigating advanced features. - Users find the **learning curve steep** , especially for newcomers unfamiliar with Google Cloud's organization and features. - Users face **complexity issues** with Gemini Enterprise Agent Platform, finding high costs and a steep learning curve challenging. - Users find the **difficult learning curve** challenging, especially for newcomers and those without structured data. #### 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:** 758 **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:** 7.7/10 (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% Enterprise, 33% Small-Business #### What Are SAS Viya's Pros and Cons? **Pros:** - Ease of Use (234 reviews) - Features (175 reviews) - Analytics (149 reviews) - Data Analysis (125 reviews) - Data Visualization (116 reviews) **Cons:** - Learning Difficulty (105 reviews) - Complexity (103 reviews) - Learning Curve (99 reviews) - Difficult Learning (82 reviews) - Expensive (78 reviews) ### What Do G2 Reviewers Say About SAS Viya? *AI-generated summary from verified user reviews* **Pros:** - Users praise the **ease of use** of SAS Viya, simplifying data visualization and decision-making processes. - Users value the **advanced analytical capabilities** of SAS Viya, enabling real-time insights and effective decision-making across industries. - Users value the **sophisticated analytical methods** in SAS Viya, enhancing decision-making through real-time deployments and ease of use. - Users appreciate the **comprehensive data analysis capabilities** of SAS Viya, enhancing insights and decision-making for business operations. - Users value the **powerful data visualization** tools of SAS Viya, enhancing insights and strategic decision-making across the organization. **Cons:** - Users find the **learning difficulty** of SAS Viya challenging, especially for those unfamiliar with SAS or cloud systems. - Users find the **complexity of setup and user management** challenging, especially for smaller IT teams and non-technical users. - Users find the **learning curve challenging** , especially for non-technical users navigating features and setting up the platform. - Users find the **difficult learning curve** of SAS Viya challenging, especially for beginners and non-technical users. - Users find the **licensing cost of SAS Viya** to be prohibitively high, impacting overall affordability and decision-making. #### What Are Recent G2 Reviews of SAS Viya? **"[Powerful & Transforming Data into Decisions—Effortlessly and Intelligently.](https://www.g2.com/survey_responses/sas-viya-review-12682824)"** **Rating:** 4.5/5.0 stars *— Venkatesh D.* [Read full review](https://www.g2.com/survey_responses/sas-viya-review-12682824) --- **"[SAS Viya is a Powerful Analytics](https://www.g2.com/survey_responses/sas-viya-review-11702846)"** **Rating:** 5.0/5.0 stars *— Deependra G.* [Read full review](https://www.g2.com/survey_responses/sas-viya-review-11702846) --- #### 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, 42% Enterprise #### What Are Snowflake's Pros and Cons? **Pros:** - Ease of Use (89 reviews) - Scalability (68 reviews) - Data Management (67 reviews) - Features (66 reviews) - Integrations (61 reviews) **Cons:** - Expensive (53 reviews) - Cost (36 reviews) - Cost Management (32 reviews) - Learning Curve (25 reviews) - Feature Limitations (21 reviews) ### What Do G2 Reviewers Say About Snowflake? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **ease of use** of Snowflake, enjoying its user-friendly interface and simplified data analysis process. - Users value the **scalability** of Snowflake, appreciating its simplicity and efficiency across various workloads and analytics tasks. - Users appreciate the **efficient data analysis** capabilities of Snowflake, simplifying data management without infrastructure concerns. - Users appreciate the **user-friendly interface** and **AI features** of Snowflake, enhancing their data management experience. - Users value the **hassle-free data integration** that Snowflake offers, significantly streamlining data management and query-writing. **Cons:** - Users find **Snowflake to be expensive** if warehouses aren’t managed properly, leading to unexpected costs. - Users indicate that managing **cost** is a significant challenge with Snowflake, often leading to unexpected bills if not monitored. - Users often struggle with **cost management** , as expenses can escalate quickly without careful monitoring and optimization. - Users face a **steep learning curve** with Snowflake, requiring time to master advanced features and UI navigation. - Users note **feature limitations** in Snowflake, especially regarding cost management and real-time data streaming capabilities. #### 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. [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:** 377 **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, 24% Mid-Market #### What Are Deepnote's Pros and Cons? **Pros:** - Ease of Use (117 reviews) - Collaboration (93 reviews) - Team Collaboration (59 reviews) - Easy Integrations (53 reviews) - Data Management (44 reviews) **Cons:** - Slow Performance (47 reviews) - Limited Features (21 reviews) - Data Management Issues (19 reviews) - Lagging Performance (18 reviews) - Slow Loading (17 reviews) ### What Do G2 Reviewers Say About Deepnote? *AI-generated summary from verified user reviews* **Pros:** - Users find Deepnote to have a **simple and intuitive interface** , facilitating effective collaboration and data analysis. - Users love the **seamless collaboration** enabled by Deepnote, enhancing teamwork and efficiency on projects. - Users love the **real-time collaboration** features of Deepnote, enhancing teamwork in analytics and data processes. - Users love the **easy integrations** in Deepnote, enabling seamless access to various tools for enhanced productivity. - Users value the **ease of data management** in Deepnote, facilitating seamless integration and efficient analytical workflows. **Cons:** - Users experience **slow performance** with large datasets, impacting the speed and reliability of their analysis. - Users find the **limited features** of Deepnote hinder their ability to effectively utilize AI and data visualization. - Users face **data management issues** with Deepnote, including slow performance and unintuitive file navigation. - Users report **lagging performance** in Deepnote, particularly when handling large datasets or multiple requests, affecting productivity. - Users experience **slow loading times** in Deepnote, which can hinder productivity and complicate project workflows. #### What Are Recent G2 Reviews of Deepnote? **"[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) --- **"[Deepnote’s Real-Time Collaboration and Cloud Notebooks Shine](https://www.g2.com/survey_responses/deepnote-review-12687317)"** **Rating:** 5.0/5.0 stars *— Jolina Mae A.* [Read full review](https://www.g2.com/survey_responses/deepnote-review-12687317) --- #### 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 ### 6. [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:** 204 **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:** 59% Enterprise, 23% Mid-Market #### What Are Dataiku's Pros and Cons? **Pros:** - Ease of Use (82 reviews) - Features (82 reviews) - Usability (46 reviews) - Easy Integrations (43 reviews) - Productivity Improvement (42 reviews) **Cons:** - Learning Curve (45 reviews) - Steep Learning Curve (26 reviews) - Slow Performance (24 reviews) - Difficult Learning (23 reviews) - Expensive (22 reviews) ### What Do G2 Reviewers Say About Dataiku? *AI-generated summary from verified user reviews* **Pros:** - Users find Dataiku's **ease of use** invaluable, simplifying complex tasks and enhancing their machine learning development experience. - Users appreciate the **user-friendly learning resources** of Dataiku, enabling easy mastery of machine learning functionalities. - Users appreciate the **user-friendly design** of Dataiku, making data collaboration and project building intuitive and simple. - Users praise the **easy integrations** of Dataiku, enabling seamless connections to various data sources and platforms. - Users value the **productivity improvement** offered by Dataiku, facilitating quick access to organized data and seamless workflows. **Cons:** - Users find the **learning curve challenging** , especially when it comes to advanced features and cloud integration issues. - Users experience a **steep learning curve** with Dataiku, making it challenging for beginners to navigate advanced features. - Users experience **slow performance** with Dataiku due to reliance on external engines and inefficiencies in handling large datasets. - Users find **difficult learning** curves due to the need for advanced technical knowledge and confusing documentation. - Users find the **expensive pricing structure** limits access for smaller companies and teams, causing budget concerns. #### What Are Recent G2 Reviews of Dataiku? **"[VisualML Potente con Limitaciones en Procesamiento Masivo](https://www.g2.com/survey_responses/dataiku-review-12982887)"** **Rating:** 5.0/5.0 stars *— Diego G.* [Read full review](https://www.g2.com/survey_responses/dataiku-review-12982887) --- **"[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) --- #### 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) ### 7. [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:** Computer Software, Information Technology and Services - **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 appreciate the **ease of use** of IBM watsonx.data, streamlining data management and enhancing analytical efficiency. - Users appreciate the **fast and easy data management** of IBM watsonx.data, enhancing efficiency with effective query execution. - Users appreciate the **flexibility and governance** of IBM watsonx.data, enhancing data management across hybrid environments. - Users appreciate the **seamless integration with other IBM tools** , enhancing workflow and efficiency in data management. - Users laud the **streamlined analytics** and unified lakehouse capabilities of IBM watsonx.data for efficient data management. **Cons:** - Users face a **steep learning curve** with watsonx.data, making initial setup and feature mastery challenging. - Users find the **complexity** of watsonx.data challenging, especially for beginners and during the setup process. - Users find the **pricing to be quite high** , making it challenging for small teams to justify investment in the product. - Users face a **difficult setup** with IBM watsonx.data, requiring careful configuration and facing a steep learning curve. - Users report experiencing **significant difficulties** with documentation and navigation, making it less beginner-friendly. #### 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) --- ### 8. [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:** 399 **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 (130 reviews) - SQL Queries (81 reviews) - Data Management (79 reviews) - SQL Querying (74 reviews) - Data Analysis (62 reviews) **Cons:** - Limited Features (45 reviews) - Missing Features (39 reviews) - Lacking Features (35 reviews) - Limited Visualization (30 reviews) - Data Management Issues (29 reviews) ### What Do G2 Reviewers Say About Hex? *AI-generated summary from verified user reviews* **Pros:** - Users find Hex to be remarkably **user-friendly** , offering seamless integration and versatile functionality for data analysis. - Users love the **ease of combining SQL with Python** , enhancing their data analysis and workflow efficiency. - Users appreciate the **seamless data management** in Hex, enabling effortless integration and collaboration across various data sources. - Users appreciate the **seamless SQL querying** capabilities of Hex, enhancing their data analysis and integration experience. - Users appreciate the **seamless integration of SQL and Python** in Hex, making data analysis quick and efficient. **Cons:** - Users note the **limited features** of Hex, expressing a need for more graph types and styling options. - Users find the **missing features** in Hex frustrating, particularly with data visualization and result tab management. - Users find Hex **lacking key features** like advanced dashboarding and limited R functionality, hindering overall efficiency. - Users find Hex's **limited visualization capabilities** frustrating, especially due to data size restrictions and inadequate filtering options. - Users find **data management issues** in Hex, including confusion with CTE, project permissions, and organization challenges. #### 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) ### 9. [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/ (575 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 value the **ease of use** of Anaconda Core, simplifying package management and installation across platforms. - Users value the **easy setup** of Anaconda Core, enabling quick startup and an intuitive experience for beginners. - Users benefit from the **high efficiency** of Anaconda Core, simplifying project management and promoting seamless data science development. - Users find the **intuitive user interface** of Anaconda Core greatly enhances their learning and project management experience. - Users appreciate the **coding ease** of Anaconda Core, enabling hassle-free package management and quick implementation. **Cons:** - Users face **data management issues** with Anaconda Core, including large installation sizes and lack of automatic backups. - Users experience **slow performance** with Anaconda Core, facing issues like high resource usage and sluggish navigation. - Users find Anaconda Core **lacking features** , with limited information on tools and overwhelming unnecessary packages. - Users find the **limited features** of Anaconda Core frustrating, particularly regarding language options and user interface improvements. - Users find the **limited storage** requirement of Anaconda Core to be a significant drawback, affecting installation and performance. #### 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) ### 10. [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:** 749 **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/ (7,860 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 appreciate the **intuitive syntax and powerful tools** of MATLAB, making data analysis and visualization simple and effective. - Users appreciate MATLAB’s **powerful data analysis and visualization tools** , enhancing their research with clear presentations and customizable plots. - Users appreciate the **excellent visualization tools** in MATLAB, allowing for clear and professional representation of data. - Users value the **extensive variety of toolboxes** in MATLAB, enhancing efficiency in complex data analysis and modeling tasks. - Users appreciate the **effective simulation capabilities** of MATLAB, enabling quick transitions from ideas to working solutions. **Cons:** - Users find MATLAB's **high licensing costs** to be a significant barrier, particularly for smaller entities and individuals. - Users often experience **slow performance** with MATLAB, especially on less powerful machines leading to lengthy execution times. - Users note the **high system requirements** of MATLAB, leading to slower performance on less powerful machines. - Users highlight the **expensive licensing** of MATLAB, creating barriers for individuals and small organizations seeking access. - Users experience **lagging performance** with MATLAB, particularly under heavy resource use and large 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 ### 11. [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:** 133 **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:** 8.6/10 (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:** 41% Small-Business, 31% Enterprise #### What Are IBM watsonx.ai's Pros and Cons? **Pros:** - Ease of Use (76 reviews) - Model Variety (31 reviews) - Features (29 reviews) - AI Integration (28 reviews) - AI Capabilities (23 reviews) **Cons:** - Difficult Learning (21 reviews) - Complexity (20 reviews) - Learning Curve (19 reviews) - Expensive (17 reviews) - Improvement Needed (16 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, praising its intuitive interface and seamless integration features. - Users value the **wide range of model types** in IBM watsonx.ai, enhancing flexibility and accelerating development processes. - Users appreciate the **user-friendly interface** of IBM watsonx.ai, enhancing efficiency in building and deploying AI models. - Users value the **user-friendly AI studio** of IBM watsonx.ai, enabling efficient chatbot creation and enhancing productivity. - Users praise the **user-friendly AI studio** of IBM watsonx.ai, appreciating its efficiency and ease of model deployment. **Cons:** - Users find a **difficult learning curve** for IBM watsonx.ai, noting that clearer guides would enhance the experience. - Users find the **complexity of IBM watsonx.ai** challenging, particularly for beginners and when customizing models. - Users find the **steep learning curve** of IBM watsonx.ai challenging, complicating setup and advanced usage for beginners. - Users find IBM watsonx.ai to be **expensive** , especially for small teams, making it less accessible and challenging to use. - Users feel that **improvement is needed** in third-party integration and the diversity of intelligent models for optimal performance. #### What Are Recent G2 Reviews of IBM watsonx.ai? **"[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) --- **"[Comprehensive AI Platform with Steep Learning Curve](https://www.g2.com/survey_responses/ibm-watsonx-ai-review-12555087)"** **Rating:** 4.5/5.0 stars *— Prashant Kumar S.* [Read full review](https://www.g2.com/survey_responses/ibm-watsonx-ai-review-12555087) --- ### 12. [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:** - AI Integration (9 reviews) - Ease of Use (7 reviews) - Integrations (6 reviews) - Fast Processing (5 reviews) - Speed (5 reviews) **Cons:** - Expensive (5 reviews) - Cost (4 reviews) - Cost Management (3 reviews) - Difficult Learning (3 reviews) - Learning Difficulty (3 reviews) ### What Do G2 Reviewers Say About Deep Learning VM Image? *AI-generated summary from verified user reviews* **Pros:** - Users love the **preconfigured AI integration** , making setup quick and efficient for deep learning and machine learning projects. - Users appreciate the **ease of use** of Deep Learning VM Image, simplifying deployment and enhancing productivity effortlessly. - Users value the **easy cloud service integrations** with Deep Learning VM Image, boosting productivity and simplifying deployment. - Users love the **fast processing** of Deep Learning VM Image, which accelerates project setup and data handling. - Users benefit from the **enhanced speed** of Deep Learning VM Image, significantly accelerating data processing and AI workflows. **Cons:** - Users find the **cost of Deep Learning VM Image** to be prohibitive, particularly for large-scale usage and services. - Users find the **high costs** associated with deep learning VM images burdensome, especially for extensive usage scenarios. - Users express concerns about **high costs associated with GPU/TPU usage** , impacting their budget for deep learning projects. - Users find the **difficult learning curve** of Deep Learning VM Image makes it challenging for beginners to use effectively. - Users find the **learning difficulty** of Deep Learning VM Image overwhelming, particularly for newcomers to the software. #### 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) --- ### 13. [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 value TensorFlow for its **powerful flexibility and scalability** , making model development and deployment seamless. - Users value the **powerful and flexible AI integration** of TensorFlow for efficient model training and deployment. - Users appreciate the **ease of use** of TensorFlow, enjoying quick model building and seamless integration. - Users praise the **variety of models** TensorFlow supports, enabling quick prototyping and scalable development in machine learning. - Users appreciate TensorFlow's **scalability** and production readiness, enabling efficient model deployment across various platforms. **Cons:** - Users find the **steep learning curve** of TensorFlow intimidating, especially for beginners trying to grasp its complexities. - Users find TensorFlow's **complexity** challenging, particularly in coding and optimizing GPU support, especially on Windows. - Users find **difficult learning** due to complex instructions and challenging debugging with cryptic error messages in TensorFlow. - Users struggle with **error handling** , as unclear messages and complex issues make debugging difficult, especially for beginners. - Users experience **slow performance** with TensorFlow, especially during smaller projects and experimentation, hindering efficient workflow. #### 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) ### 14. [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/ (156,424 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) --- ### 15. [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:** 565 **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/ (449 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, 27% 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 value the **ease of use** of Posit Team, facilitating smooth workflows and effortless exploration of data. - Users value Posit's **cutting-edge features** and excellent documentation, fostering confidence and efficiency in their workflows. - Users value Posit's **commitment to open source software** , enhancing collaboration and accessibility in the community. - Users praise the **exceptional customer support** of Posit Team, ensuring assistance whenever needed for smooth project management. - Users appreciate the **easy integrations** of Posit Team, enabling seamless setup with tools like GitHub and RStudio. **Cons:** - Users experience **slow performance** with Posit Team, facing significant loading times and delays that hinder productivity. - Users struggle with a **steep learning curve** in Posit, making it challenging for new users to adapt quickly. - Users experience **performance issues** with Posit, facing slow loading times and delays that hinder productivity. - Users face a **steep learning curve** with Posit Team, making it challenging for new users to adapt quickly. - Users report **lagging performance** with large datasets and slow loading times, impacting overall productivity and user experience. #### 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 ### 16. [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:** 821 **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, finding it simple to automate complex tasks through drag and drop. - Users appreciate Alteryx for its **automation capabilities** , enhancing data handling efficiency and enabling deeper analytical insights. - Users find Alteryx to be **very intuitive** , making it easy for non-technical individuals to learn and use effectively. - Users find that Alteryx offers **easy learning** , making technology accessible for non-technical individuals through intuitive design. - Users commend Alteryx for its **high efficiency** in managing data, streamlining workflows and saving valuable time. **Cons:** - Users find the **license cost to be expensive** , making it challenging for small teams and startups to justify. - Users face a **learning curve** that can hinder beginners as they explore Alteryx's advanced features effectively. - Users find **missing features** in Alteryx, such as limited reporting and absence of Mac compatibility, frustrating. - Users note the **steep learning curve** of Alteryx, especially for those familiar with SQL and RegEx. - Users report **slow performance** with Alteryx, particularly when handling large workflows, causing significant analysis delays. #### What Are Recent G2 Reviews of Alteryx? **"[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) --- **"[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) --- ### 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:** 52 **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/ (156,424 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 appreciate the **brilliant ease of use** of Amazon SageMaker, enabling quick adaptation and efficient model training. - Users appreciate the **seamless AI integration** of Amazon SageMaker, enhancing model development and deployment effortlessly. - Users praise the **superior computing power** of Amazon SageMaker, significantly reducing model training time and enhancing efficiency. - Users find Amazon SageMaker to be **highly efficient** , significantly reducing model training time and simplifying the user experience. - Users love the **fast processing** of Amazon SageMaker, significantly reducing model training time and enhancing productivity. **Cons:** - Users find Amazon SageMaker **expensive and complex** , especially for long-term projects and high-demand deployments. - Users find the **complex pricing structure** of Amazon SageMaker challenging, often leading to unexpected costs and confusion. - Users find that the **complex pricing structure** of Amazon SageMaker can quickly lead to unexpected costs and confusion. - Users find the **steep learning curve** challenging, particularly for newcomers to AWS services and configurations. - Users find the **difficult learning** curve during initial setup of Amazon SageMaker to be a significant challenge. #### What Are Recent G2 Reviews of Amazon SageMaker? **"[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) --- **"[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) --- #### 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 appreciate the **seamless AI integration** of Wipro Holmes, enhancing efficiency and automating complex business processes effortlessly. - Users commend Wipro Holmes for its **efficient automation** , enhancing productivity and decision-making with advanced AI capabilities. - Users commend Wipro Holmes for its **superb efficiency enhancement** through AI and automation, streamlining business processes effectively. - Users commend Wipro HOLMES for its **analysis efficiency** , which streamlines tasks and enhances decision-making capabilities. - Users appreciate the **enhanced efficiency through AI and automation** in Wipro Holmes, streamlining processes and reducing costs. **Cons:** - Users desire a more **intuitive user interface** for Wipro Holmes to aid non-technical users in managing automation. - Users desire more **customization options** in Wipro Holmes to better tailor the product to their needs. - Users find that the **steep learning curve** for advanced features may require technical assistance, hindering their experience. #### 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 (15 reviews) - GPU Performance (12 reviews) - Computing Power (10 reviews) - Setup Ease (9 reviews) - Easy Integrations (8 reviews) **Cons:** - Expensive (6 reviews) - Complexity Issues (4 reviews) - Poor Documentation (4 reviews) - Difficult Setup (3 reviews) - Insufficient Learning Resources (3 reviews) ### What Do G2 Reviewers Say About Saturn Cloud? *AI-generated summary from verified user reviews* **Pros:** - Users find Saturn Cloud to be **exceptionally easy to use** , with simple setup and effective tools for their projects. - Users benefit from the **powerful GPU performance** of Saturn Cloud, significantly enhancing development speed and efficiency. - Users value the **powerful GPU computing resources** of Saturn Cloud, enhancing their machine learning and project capabilities. - Users praise the **setup ease** of Saturn Cloud, noting its convenience and quick configuration for GPU resources. - Users value the **easy integrations** of Saturn Cloud, making it simple to connect with various resources for their projects. **Cons:** - Users find Saturn Cloud's pricing to be **expensive** and suggest the need for more affordable plans or free options. - Users experience **complexity issues** with Saturn Cloud, particularly regarding pricing and advanced documentation for beginners. - Users find the **documentation lacking** , making it difficult for beginners to effectively utilize Saturn Cloud's features. - Users find the **difficult setup** process of Saturn Cloud challenging, although later usage improves after initial configuration. - Users find the **insufficient learning resources** challenging, particularly for beginners navigating complex setups and documentation. #### 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 **brilliant user interface** of Cloudera, highlighting its ease of use for big data management. - Users praise Cloudera for its **easy scalability** , effectively managing large data volumes with seamless performance. - Users value the **robust security** features of Cloudera, ensuring dependable and safe data management for their analytics needs. - Users appreciate the **comprehensive tools** of Cloudera, enhancing their experience in big data management and analytics. - Users value the **scalability and ease of use** of Cloudera, enhancing data processing and administration efficiency. **Cons:** - Users note that Cloudera is quite **expensive** , with high costs and a small team required for effective management. - Users find Cloudera's DB to be **highly complex** , making SQL queries and customization challenging, especially for beginners. - Users find Cloudera's setup a bit **difficult to learn** , especially for beginners needing clearer guidance and tutorials. - Users find the **poor documentation** of Cloudera challenging, impacting their ability to navigate and troubleshoot effectively. - Users experience **access issues** with Cloudera, facing unauthorized errors and limited documentation support, affecting usability. #### 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** , benefiting from its intuitive interface and supportive resources. - Users appreciate the **scalability and integration** of Azure Machine Learning, enhancing their machine learning projects effortlessly. - Users appreciate the **awesome customer support** from Azure Machine Learning, complemented by extensive documentation and community resources. - Users appreciate the **easy-to-use data management features** in Azure Machine Learning, facilitating effective data organization and pattern recognition. - Users find Azure Machine Learning to be an **efficient environment** for launching and monitoring machine learning jobs effortlessly. **Cons:** - Users face a challenging **learning curve** with Azure Machine Learning, requiring time to master its tools and interface. - Users find the **difficult navigation** in Azure Machine Learning frustrating, often requiring excessive clicks to locate options. - Users find the **disordered user interface** of Azure Machine Learning complicates navigation and hinders productivity. - Users find the **complex interface** of Azure Machine Learning difficult, noting non-intuitive flows and missing features. - Users report a significant **learning curve** with Azure Machine Learning, making it challenging for newcomers to navigate 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 praise the **ease of use** of Pecan, allowing quick model building without deep technical knowledge. - Users commend the **excellent customer support** from Pecan, providing prompt assistance and valuable guidance throughout the process. - Users appreciate the **speed of development** with Pecan, significantly reducing model creation time from months to weeks. - Users highlight Pecan's **exceptional support and efficient problem-solving** , enhancing their data-driven decision-making processes. - Users find Pecan provides **implementation ease** , accelerating model development with excellent support for a swift production transition. **Cons:** - Users note a **steep learning curve** with Pecan, particularly regarding data structure and SQL proficiency. - Users desire more **control over model selection** and customization options for specific use cases and optimization metrics. - Users feel the **limited features** restrict their ability to customize models and optimize for specific use cases. - Users face a **learning curve** initially, needing to grasp data structures and SQL for effective use of Pecan. - Users desire **more customization options** in Pecan, wishing for deeper control over model selection and optimization metrics. #### 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. [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 appreciate the **ease of use** of Altair AI Studio, enjoying its intuitive drag-and-drop interface for complex tasks. - Users enjoy the **ease of use for machine learning** in Altair AI Studio, enabling effective model creation without coding. - Users commend the **advanced AI integration** of Altair AI Studio, enhancing decision-making and streamlining data processes effectively. - Users value the **advanced machine learning and data analytics** of Altair AI Studio for smarter decision making and efficiency. - Users appreciate the **automation capabilities** of Altair AI Studio, significantly improving efficiency in data processing and analytics. **Cons:** - Users find the **complexity** of Altair AI Studio challenging due to language barriers and integration issues. - Users report **slow performance and freeze issues** when managing large datasets in Altair AI Studio, impacting usability. - Users frequently experience **slow performance** with large datasets, leading to frustrating slowdowns and occasional freezes. - Users find Altair AI Studio **complex and hard to understand** , particularly due to limited support in Japanese. - Users find the **complex usage** of Altair AI Studio challenging, especially with inadequate documentation and support in Japanese. #### 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) ### 24. [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 **strong analysis capabilities** of IBM SPSS Modeler, enhancing data manipulation and integration for optimal BI use. - Users appreciate the **robust analytical capabilities** of IBM SPSS Modeler, enhancing data manipulation and integration for BI tools. - Users value the **direct data access** of IBM SPSS Modeler, enhancing seamless data manipulation and integration. - Users value the **seamless data manipulation** capabilities of IBM SPSS Modeler for effective analysis and integration. - Users value the **data visualization capabilities** of IBM SPSS Modeler, enabling seamless data manipulation and analysis. **Cons:** - Users feel that the **high licensing costs** of IBM SPSS Modeler make it an expensive option for each user. - Users find the **licensing costs prohibitively high** , making it expensive for each individual user to access. #### 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) ### 25. [IBM Decision Optimization](https://www.g2.com/products/ibm-decision-optimization/reviews) IBM Decision Optimization is a family of prescriptive analytics products that combines mathematical and AI techniques to help with business decision-making including operational, tactical and strategic planning and scheduling use cases. The solutions enable business decision-makers to choose the optimal course of action from millions of alternatives when faced with decisions that involve multiple variables, trade-off possibilities and complex constraints. The solution incorporates powerful optimization solvers namely CPLEX Optimizer and CP Optimizer to solve the breadth of optimization problems including mathematical and constraint programming and constraint-based scheduling models. Learn more about this portfolio here https://www.ibm.com/analytics/decision-optimization IBM ILOG CPLEX Optimization Studio is one of the products within the IBM Decision Optimization portfolio. Organizations across industries are using IBM ILOG CPLEX Optimization Studio to drive operational efficiency and generate significant ROI by optimizing planning, scheduling, pricing and other business decisions. The offering provides users the flexibility to develop optimization models either using general programming language APIs like Python, Java, or using Optimization Programming Language (OPL). The powerful CPLEX optimization engines can deliver the power necessary to solve very large, real- world optimization problems at the speed required for today’s interactive decision optimization applications. Learn more about this product here - https://www.ibm.com/products/ilog-cplex-optimization-studio IBM Decision Optimization is also included within Watson Studio Premium for Cloud Pak for Data to enable data science teams to capitalize on the power of prescriptive analytics and build innovative solutions using a combination of techniques like machine learning and optimization. Data science teams can easily demonstrate business value of optimization by leveraging tools like visual dashboards & modeling assistant to quickly build models, test/evaluate multiple scenarios, solve using powerful optimization engines and deploy the models easily . IBM Decision Optimization solutions bring more than 30 years of experience in the field and is a proven optimization technology and organizations across industries are using IBM Decision Optimization solutions to run their mission-critical decision-making applications and have benefited by way of reduction in operating costs, increase in revenue and accelerated time to value. **Average Rating:** 4.5/5.0 **Total Reviews:** 35 **How Do G2 Users Rate IBM Decision Optimization?** - **Application:** 8.3/10 (Category avg: 8.5/10) - **Managed Service:** 8.3/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 7.3/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.7/10 (Category avg: 8.5/10) **Who Is the Company Behind IBM Decision Optimization?** - **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:** Computer Software, Financial Services - **Company Size:** 59% Enterprise, 22% Small-Business #### What Are Recent G2 Reviews of IBM Decision Optimization? **"[leveraging data resources](https://www.g2.com/survey_responses/ibm-decision-optimization-review-8714924)"** **Rating:** 4.5/5.0 stars *— James C.* [Read full review](https://www.g2.com/survey_responses/ibm-decision-optimization-review-8714924) --- **"[In-depth analytics.. for a price.](https://www.g2.com/survey_responses/ibm-decision-optimization-review-8681163)"** **Rating:** 4.5/5.0 stars *— Andrew M.* [Read full review](https://www.g2.com/survey_responses/ibm-decision-optimization-review-8681163) --- #### What Are G2 Users Discussing About IBM Decision Optimization? - [What is IBM Decision Optimization used for?](https://www.g2.com/discussions/what-is-ibm-decision-optimization-used-for) - [What is IBM ILOG cplex optimization studio?](https://www.g2.com/discussions/what-is-ibm-ilog-cplex-optimization-studio) - [Which IBM offering would be considered best for Prescriptive Analytics?](https://www.g2.com/discussions/which-ibm-offering-would-be-considered-best-for-prescriptive-analytics) - [What is decision optimization?](https://www.g2.com/discussions/what-is-decision-optimization) - [What is IBM decision optimization?](https://www.g2.com/discussions/what-is-ibm-decision-optimization) ## 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.