# Best Data Science and Machine Learning Platforms - Page 2 *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 (205 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. [IBM Watson Studio](https://www.g2.com/products/ibm-watson-studio/reviews) IBM Watson Studio on IBM Cloud Pak for Data is a leading data science and machine learning solution that helps enterprises accelerate AI-powered digital transformation. It allows businesses to scale trustworthy AI and optimize decisions. Build, run, and manage AI models on any cloud through an automated end-to-end AI lifecycle--simplifying experimentation and deployment, speeding up data exploration and preparation, and improving model development and training. Govern and monitor models to mitigate drift and bias, and manage model risk. Build a ModelOps practice that synchronizes application and model pipelines to operationalize responsible, explainable AI across your enterprise. As a key offering of IBM Cloud Pak for Data, a unified data and AI platform, Watson Studio integrates seamlessly with data management services, data privacy and security capabilities, AI application tooling, open source frameworks, and a robust technology ecosystem. It unites teams and empowers businesses to build the modern information architecture that AI requires and infuse it across the organization. IBM Watson Studio is code-optional, allowing both data scientists and business analysts to work on the same platform by providing the best of open source tools along with visual, drag-and-drop capabilities. It enables organizations to tap into data assets and inject predictions into business processes and modern applications—helping them maximize their business value. It's suited for hybrid multicloud environments that demand mission-critical performance, security, and governance. Features include: • AutoAI that eliminates time-consuming, repetitive tasks by automating data preparation, model development, feature engineering and hyperparameter optimization. • Text Analytics for uncovering insights from unstructured data • Drag-and-drop visual model-building with SPSS Modeler • Broad data access – flat files, spreadsheets, major relational databases • Sophisticated graphics engine for building stunning visualizations • Support for Python 3 Notebooks Watson Studio is available via several deployment options: • IBM Cloud Pak for Data – An open, extensible data and AI platform that runs on any cloud • IBM Cloud Pak for Data System – A hybrid cloud, on-premises platform-in-a-box • IBM Cloud Pak for Data as a Service – A set of IBM Cloud Pak for Data platform services fully managed on the IBM Cloud **Average Rating:** 4.2/5.0 **Total Reviews:** 161 **How Do G2 Users Rate IBM Watson Studio?** - **Application:** 9.2/10 (Category avg: 8.5/10) - **Managed Service:** 9.3/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.9/10 (Category avg: 8.2/10) - **Ease of Admin:** 7.8/10 (Category avg: 8.5/10) **Who Is the Company Behind IBM Watson Studio?** - **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?** - **Who Uses This:** Software Engineer, CEO - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 50% Enterprise, 31% Small-Business #### What Are IBM Watson Studio's Pros and Cons? **Pros:** - AI Capabilities (4 reviews) - AI Technology (4 reviews) - Ease of Use (4 reviews) - Machine Learning (4 reviews) - AI Integration (3 reviews) **Cons:** - Expensive (3 reviews) - Learning Curve (3 reviews) - Steep Learning Curve (3 reviews) - Complex Interface (1 reviews) - Complexity (1 reviews) ### What Do G2 Reviewers Say About IBM Watson Studio? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **Auto AI capability** of IBM Watson Studio, streamlining workflows and enhancing productivity in data science projects. - Users appreciate the **Auto AI capability** of IBM Watson Studio, significantly reducing manual work and enhancing efficiency. - Users appreciate the **user-friendly interface** of IBM Watson Studio, which makes starting and collaborating on projects effortless. - Users appreciate the **Auto AI capability** of IBM Watson Studio, which significantly enhances efficiency in data preprocessing and model selection. - Users appreciate the **easy AI integration** in IBM Watson Studio, enhancing their experience with seamless data management and deployment. **Cons:** - Users find the **high cost** of IBM Watson Studio a barrier, especially for individuals and small startups. - Users face a **steep learning curve** with IBM Watson Studio, making it challenging for beginners to navigate its complexities. - Users find the **steep learning curve** of IBM Watson Studio challenging, especially for those new to the platform. - Users find the **complex interface** of IBM Watson Studio challenging, particularly for those just starting out. - Users find the **complexity** of IBM Watson Studio challenging, particularly due to its steep learning curve and intricate interface. #### What Are Recent G2 Reviews of IBM Watson Studio? **"[Robust Platform for Seamless Data Science Collaboration](https://www.g2.com/survey_responses/ibm-watson-studio-review-12313951)"** **Rating:** 4.5/5.0 stars *— Naimish M.* [Read full review](https://www.g2.com/survey_responses/ibm-watson-studio-review-12313951) --- **"[Game-Changing Experience with AutoAI and ModelOps in IBM Watson Studio](https://www.g2.com/survey_responses/ibm-watson-studio-review-12176009)"** **Rating:** 4.5/5.0 stars *— Verified User in Higher Education* [Read full review](https://www.g2.com/survey_responses/ibm-watson-studio-review-12176009) --- #### What Are G2 Users Discussing About IBM Watson Studio? - [What is IBM Watson Studio used for?](https://www.g2.com/discussions/what-is-ibm-watson-studio-used-for) - 1 upvote - [What are the main benefits of using AutoAI in IBM Watson Studio?](https://www.g2.com/discussions/what-are-the-main-benefits-of-using-autoai-in-ibm-watson-studio) - [Is IBM Watson Studio free?](https://www.g2.com/discussions/is-ibm-watson-studio-free) - [How do I use IBM Watson Studio?](https://www.g2.com/discussions/how-do-i-use-ibm-watson-studio) - [What does IBM Watson Studio do?](https://www.g2.com/discussions/what-does-ibm-watson-studio-do) ### 2. [RapidCanvas](https://www.g2.com/products/rapidcanvas/reviews) RapidCanvas takes enterprise AI from concept to production to scale. Our Hybrid Approach™ pairs elite human experts with a purpose-built agentic platform. We partner to create custom solutions that are human-led, agent-executed, and built to deliver real outcomes from day one. Most enterprise AI breaks because it fails to solve key problems of context and execution. RapidCanvas is built around solving both. Every engagement starts with your own Enterprise Context Engine™: a living intelligence at the heart of your business. It unifies your data, maps your workflows, and learns from every use case. The more you build, the smarter it gets. Every iteration feeds the engine, and the intelligence compounds, making successful execution at an enterprise scale possible. RapidCanvas serves industry leaders in manufacturing, retail and consumer goods, financial services, supply chain, and infrastructure. Solutions are enterprise-grade, scalable from the start, and compliant with the highest standards for security and privacy. Future-proof by design. Outcome-led by default. **Average Rating:** 4.8/5.0 **Total Reviews:** 38 **How Do G2 Users Rate RapidCanvas?** - **Application:** 9.5/10 (Category avg: 8.5/10) - **Managed Service:** 9.2/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.6/10 (Category avg: 8.2/10) - **Ease of Admin:** 9.3/10 (Category avg: 8.5/10) **Who Is the Company Behind RapidCanvas?** - **Seller:** [RapidCanvas](https://www.g2.com/sellers/rapidcanvas) - **Company Website:** https://rapidcanvas.ai/ - **Year Founded:** 2021 - **HQ Location:** Austin, Texas - **Twitter:** @rapidcanvas (86 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/rapidcanvas/ (101 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 42% Small-Business, 37% Mid-Market #### What Are RapidCanvas's Pros and Cons? **Pros:** - Ease of Use (6 reviews) - Time-saving (4 reviews) - User Interface (4 reviews) - AI Integration (3 reviews) - Customer Support (3 reviews) **Cons:** - Slow Performance (3 reviews) - Limited Customization (2 reviews) - Complexity (1 reviews) - Difficult Setup (1 reviews) - Insufficient Learning Resources (1 reviews) ### What Do G2 Reviewers Say About RapidCanvas? *AI-generated summary from verified user reviews* **Pros:** - Users admire the **ease of use** of RapidCanvas, enabling efficient data visualization without advanced technical skills. - Users value the **time-saving capabilities** of RapidCanvas, transforming data visualization into a quick and seamless process. - Users commend the **intuitive and accessible user interface** of RapidCanvas, facilitating quick and efficient data visualization. - Users highlight the **versatile AI integration** in RapidCanvas, facilitating seamless development of innovative solutions and enhancing productivity. - Users highlight the **excellent customer support** of RapidCanvas, emphasizing its responsiveness and helpfulness for smooth operations. **Cons:** - Users often experience **slow performance** while handling large datasets, which impacts efficiency during data processing tasks. - Users find **limited customization** options in RapidCanvas frustrating, especially for advanced needs and dashboard flexibility. - Users find the **complexity of setup and AI training** to be a significant challenge in using RapidCanvas. - Users struggle with the **difficult setup** of RapidCanvas, facing challenges in data extraction and AI training. - Users note that **insufficient learning resources** hinder deeper understanding and utilization of RapidCanvas's advanced features. #### What Are Recent G2 Reviews of RapidCanvas? **"[From Ambition to Achievement!](https://www.g2.com/survey_responses/rapidcanvas-review-12590997)"** **Rating:** 5.0/5.0 stars *— Kristianne L.* [Read full review](https://www.g2.com/survey_responses/rapidcanvas-review-12590997) --- **"[Effortlessly Transformed Our Manual Processes](https://www.g2.com/survey_responses/rapidcanvas-review-12672099)"** **Rating:** 4.5/5.0 stars *— Andrew F.* [Read full review](https://www.g2.com/survey_responses/rapidcanvas-review-12672099) --- ### 3. [Teradata Autonomous Knowledge Platform](https://www.g2.com/products/teradata-autonomous-knowledge-platform/reviews) Teradata Autonomous Knowledge Platform activates enterprise intelligence by unifying data, knowledge and business context to achieve tangible outcomes. With Teradata, organizations can provide agents with full context for impact when it matters. Our solution lets businesses connect and scale on premises, in the cloud, or through a hybrid approach. Teradata delivers real business value with AI. Learn more at Teradata.com. **Average Rating:** 4.3/5.0 **Total Reviews:** 355 **How Do G2 Users Rate Teradata Autonomous Knowledge Platform?** - **Application:** 8.3/10 (Category avg: 8.5/10) - **Managed Service:** 8.3/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 7.9/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.0/10 (Category avg: 8.5/10) **Who Is the Company Behind Teradata Autonomous Knowledge Platform?** - **Seller:** [Teradata Autonomous Knowledge Platform](https://www.g2.com/sellers/teradata-autonomous-knowledge-platform) - **Company Website:** https://www.teradata.com - **Year Founded:** 1979 - **HQ Location:** San Diego, CA - **Twitter:** @Teradata (93,113 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1466/ (9,880 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Engineer, Software Engineer - **Top Industries:** Information Technology and Services, Financial Services - **Company Size:** 69% Enterprise, 22% Mid-Market #### What Are Teradata Autonomous Knowledge Platform's Pros and Cons? **Pros:** - Performance (14 reviews) - Analytics (11 reviews) - Scalability (11 reviews) - Speed (11 reviews) - Large Datasets (9 reviews) **Cons:** - Learning Curve (9 reviews) - Steep Learning Curve (5 reviews) - Complexity (4 reviews) - Cost (3 reviews) - Expensive (3 reviews) ### What Do G2 Reviewers Say About Teradata Autonomous Knowledge Platform? *AI-generated summary from verified user reviews* **Pros:** - Users highly value the **extreme performance** of the Teradata Autonomous Knowledge Platform for processing large data volumes efficiently. - Users commend the **high performance of analytics** in Teradata, enabling reliable business solutions and seamless data integration. - Users value the **scalability** of Teradata Autonomous Knowledge Platform, enabling efficient data integration and high performance. - Users value the **extreme performance** of Teradata, benefiting from fast processing and seamless data integration. - Users value the **fast processing of large datasets** with Teradata, ensuring reliable and efficient operations. **Cons:** - Users face a **steep learning curve** with Teradata Autonomous Knowledge Platform, impacting initial productivity and ease of use. - Users find the **steep learning curve** of Teradata Autonomous Knowledge Platform challenging, particularly for newcomers without technical expertise. - Users find the **complexity** of Teradata Autonomous Knowledge Platform challenging, especially for those without technical expertise. - Users face challenges with **cost management** , highlighting the need for transparency to avoid misusage and performance issues. - Users express concern over the **high cost** of the Teradata Autonomous Knowledge Platform, highlighting the need for better cost optimization. #### What Are Recent G2 Reviews of Teradata Autonomous Knowledge Platform? **"[Teradata Vantage Fast Query Performance and Strong Analytics for Big Data](https://www.g2.com/survey_responses/teradata-autonomous-knowledge-platform-review-12821668)"** **Rating:** 5.0/5.0 stars *— Muzammil M.* [Read full review](https://www.g2.com/survey_responses/teradata-autonomous-knowledge-platform-review-12821668) --- **"[Teradata Vantage Excels at Big Data Processing and Advanced Analytics](https://www.g2.com/survey_responses/teradata-autonomous-knowledge-platform-review-12739181)"** **Rating:** 4.5/5.0 stars *— Nijat I.* [Read full review](https://www.g2.com/survey_responses/teradata-autonomous-knowledge-platform-review-12739181) --- #### What Are G2 Users Discussing About Teradata Autonomous Knowledge Platform? - [What does Teradata Data Lab do?](https://www.g2.com/discussions/what-does-teradata-data-lab-do) - [Is Teradata a premiership?](https://www.g2.com/discussions/is-teradata-a-premiership) - [What is Teradata Vantage?](https://www.g2.com/discussions/what-is-teradata-vantage) - [How much does Teradata cost?](https://www.g2.com/discussions/how-much-does-teradata-cost) - [What is Sandbox in Teradata?](https://www.g2.com/discussions/what-is-sandbox-in-teradata) ### 4. [Google Cloud AutoML](https://www.g2.com/products/google-cloud-automl/reviews) Google Cloud AutoML is a suite of machine learning products designed to enable developers with limited expertise to train high-quality custom models tailored to their specific business needs. By leveraging Google's advanced transfer learning and neural architecture search technologies, AutoML simplifies the process of building, deploying, and scaling machine learning models, making AI more accessible to a broader audience. Key Features and Functionality: - Automated Model Training: AutoML automates the selection of model architecture and hyperparameter tuning, reducing the need for manual intervention and specialized knowledge. - User-Friendly Interface: The platform offers an intuitive graphical interface that allows users to upload data, train models, and manage deployments with ease. - Versatile Model Types: AutoML supports various data types and tasks through specialized services: - AutoML Vision: For image classification and object detection. - AutoML Natural Language: For text classification, sentiment analysis, and entity recognition. - AutoML Translation: For creating custom translation models between language pairs. - AutoML Video Intelligence: For video classification and object tracking. - AutoML Tables: For structured data tasks like regression and classification. - Seamless Integration: AutoML integrates with other Google Cloud services, facilitating efficient data management, model deployment, and scalability. Primary Value and Problem Solving: Google Cloud AutoML democratizes machine learning by enabling users without deep technical expertise to develop and deploy custom models. This accessibility allows businesses to harness the power of AI to solve complex problems, such as improving customer experiences through personalized recommendations, automating content moderation, enhancing language translation services, and gaining insights from large datasets. By reducing the barriers to entry, AutoML empowers organizations to innovate and stay competitive in their respective industries. **Average Rating:** 4.1/5.0 **Total Reviews:** 22 **How Do G2 Users Rate Google Cloud AutoML?** - **Natural Language Understanding:** 10.0/10 (Category avg: 8.2/10) - **Ease of Admin:** 7.9/10 (Category avg: 8.5/10) **Who Is the Company Behind Google Cloud AutoML?** - **Seller:** [Google](https://www.g2.com/sellers/google) - **Year Founded:** 1998 - **HQ Location:** Mountain View, CA - **Twitter:** @google (31,899,995 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (341,888 employees on LinkedIn®) - **Ownership:** NASDAQ:GOOG **Who Uses This Product?** - **Company Size:** 45% Small-Business, 27% Enterprise #### What Are Google Cloud AutoML's Pros and Cons? **Pros:** - AI Integration (1 reviews) - Ease of Use (1 reviews) - Easy Integrations (1 reviews) - Integrated Platform (1 reviews) - Intuitive (1 reviews) **Cons:** - Cost (1 reviews) - Expensive (1 reviews) ### What Do G2 Reviewers Say About Google Cloud AutoML? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **seamless AI integration** of Google Cloud AutoML, enabling easy training of high-quality models. - Users value the **ease of use** of Google Cloud AutoML, appreciating its intuitive interface and clear documentation. - Users love the **easy integrations** with other Google Cloud services, enhancing their machine learning experience effortlessly. - Users appreciate the **seamless integration** of Google Cloud AutoML with other Google services, enhancing their machine learning experience. - Users find the **intuitive interface** of Google Cloud AutoML simplifies training high-quality machine learning models effortlessly. **Cons:** - Users find the **pricing expensive** , which can be a barrier for small projects or students. - Users find the **pricing expensive** for small projects or students, limiting accessibility and affordability. #### What Are Recent G2 Reviews of Google Cloud AutoML? **"[Fast and reliable AutoML platform for building ML model with case](https://www.g2.com/survey_responses/google-cloud-automl-review-11665221)"** **Rating:** 4.5/5.0 stars *— Verified User in Computer Software* [Read full review](https://www.g2.com/survey_responses/google-cloud-automl-review-11665221) --- **"[Google Cloud AutoML: Powerful performance and efficient ML kit](https://www.g2.com/survey_responses/google-cloud-automl-review-3004089)"** **Rating:** 4.5/5.0 stars *— Ravi P.* [Read full review](https://www.g2.com/survey_responses/google-cloud-automl-review-3004089) --- #### What Are G2 Users Discussing About Google Cloud AutoML? - [What is Google Cloud AutoML used for?](https://www.g2.com/discussions/what-is-google-cloud-automl-used-for) ### 5. [Qlik Predict](https://www.g2.com/products/qlik-predict/reviews) Qlik AutoML (automated machine learning) brings AI-generated machine learning models and predictive analytics directly to your organization’s larger community of analytics users and teams, in a simple user experience focused on augmenting their intuition through machine intelligence. With AutoML, you can easily generate machine learning models, make predictions, and plan decisions – all within an intuitive, code-free user interface. Machine learning (ML) is a branch of artificial intelligence (AI) focused on the process of recognizing patterns in historical data to predict outcomes in the future. ML uses historically observed data as an input, applies a mathematical process against that data, and creates an output called a machine learning model based on patterns in historical data. This model can then be used to make future predictions and test scenarios. **Average Rating:** 4.4/5.0 **Total Reviews:** 78 **How Do G2 Users Rate Qlik Predict?** - **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:** 8.7/10 (Category avg: 8.5/10) **Who Is the Company Behind Qlik Predict?** - **Seller:** [Qlik](https://www.g2.com/sellers/qlik) - **Year Founded:** 1993 - **HQ Location:** Radnor, PA - **Twitter:** @qlik (64,130 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/10162/ (4,551 employees on LinkedIn®) - **Phone:** 1 (888) 994-9854 **Who Uses This Product?** - **Who Uses This:** Data Analyst - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 38% Enterprise, 31% Small-Business #### What Are Qlik Predict's Pros and Cons? **Pros:** - Automation (5 reviews) - Ease of Use (5 reviews) - AI Integration (4 reviews) - Machine Learning (4 reviews) - AI Capabilities (3 reviews) **Cons:** - Limited Customization (4 reviews) - Deployment Issues (2 reviews) - Lacking Features (2 reviews) - Required Knowledge (2 reviews) - Tool Limitations (2 reviews) ### What Do G2 Reviewers Say About Qlik Predict? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **automation capabilities** of Qlik Predict, enabling quick and easy predictive model development with minimal effort. - Users appreciate the **ease of use** of Qlik Predict, enabling no-code project implementation and intuitive model creation. - Users appreciate the **seamless AI integration** of Qlik Predict, enabling rapid development of predictive models without technical barriers. - Users value the **intuitive no-code interface** of Qlik Predict, enabling quick and easy machine learning model creation. - Users appreciate the **user-friendly AI capabilities** of Qlik Predict, enabling seamless predictive analytics without coding. **Cons:** - Users find the **limited customization** of Qlik Predict restrictive, especially for advanced machine learning needs. - Users experience **deployment issues** due to limited integration options with external workflows outside the Qlik ecosystem. - Users find **Qlik Predict lacking features** such as flexibility in deployment options and model customization, limiting advanced use. - Users find the **required knowledge for optimization** challenging, impacting effective utilization of Qlik Predict's capabilities. - Users find **flexibility limitations** in Qlik Predict, as the no-code approach hinders advanced customization and model control. #### What Are Recent G2 Reviews of Qlik Predict? **"[Qlik AutoML](https://www.g2.com/survey_responses/qlik-predict-review-11001365)"** **Rating:** 4.0/5.0 stars *— Anju P.* [Read full review](https://www.g2.com/survey_responses/qlik-predict-review-11001365) --- **"[Describe your experience in one short sentence.](https://www.g2.com/survey_responses/qlik-predict-review-11007278)"** **Rating:** 5.0/5.0 stars *— Cristian C.* [Read full review](https://www.g2.com/survey_responses/qlik-predict-review-11007278) --- #### What Are G2 Users Discussing About Qlik Predict? - [How does the NZXT Kraken work?](https://www.g2.com/discussions/how-does-the-nzxt-kraken-work) - [Is the NZXT Kraken water cooling?](https://www.g2.com/discussions/is-the-nzxt-kraken-water-cooling) - [Which NZXT Kraken is the best?](https://www.g2.com/discussions/which-nzxt-kraken-is-the-best) - [Does the NZXT Kraken come with fans?](https://www.g2.com/discussions/does-the-nzxt-kraken-come-with-fans) ### 6. [BigML](https://www.g2.com/products/bigml/reviews) Enjoy the power of Programmatic Machine Learning **Average Rating:** 4.7/5.0 **Total Reviews:** 24 **How Do G2 Users Rate BigML?** - **Application:** 8.3/10 (Category avg: 8.5/10) - **Managed Service:** 9.2/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 9.2/10 (Category avg: 8.2/10) - **Ease of Admin:** 9.3/10 (Category avg: 8.5/10) **Who Is the Company Behind BigML?** - **Seller:** [BigML](https://www.g2.com/sellers/bigml) - **Year Founded:** 2011 - **HQ Location:** Corvallis, OR - **Twitter:** @bigmlcom (6,077 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1742510 (30 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Senior Software Engineer, Software Engineer - **Top Industries:** Computer Software - **Company Size:** 88% Small-Business, 8% Mid-Market #### What Are Recent G2 Reviews of BigML? **"[Machine Learning Platform with cloud based for Data processing](https://www.g2.com/survey_responses/bigml-review-8031197)"** **Rating:** 4.5/5.0 stars *— Nitin Y.* [Read full review](https://www.g2.com/survey_responses/bigml-review-8031197) --- **"[My Experience about BigML](https://www.g2.com/survey_responses/bigml-review-8076641)"** **Rating:** 4.5/5.0 stars *— Siddharth S.* [Read full review](https://www.g2.com/survey_responses/bigml-review-8076641) --- ### 7. [Domo](https://www.g2.com/products/domo/reviews) Domo's AI and Data Products Platform empowers organizations to turn data into actionable insights and solutions. It allows users to seamlessly connect diverse data sources, prepare data for use, and generate dynamic reports and visualizations—all within a single interface. With built-in AI and automation capabilities, teams can easily build and use AI agents, streamline workflows, and create tailored solutions. **Average Rating:** 4.3/5.0 **Total Reviews:** 994 **How Do G2 Users Rate Domo?** - **Application:** 5.9/10 (Category avg: 8.5/10) - **Managed Service:** 6.4/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 5.8/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.1/10 (Category avg: 8.5/10) **Who Is the Company Behind Domo?** - **Seller:** [Domo](https://www.g2.com/sellers/domo) - **Company Website:** https://www.domo.com - **Year Founded:** 2010 - **HQ Location:** American Fork, UT - **Twitter:** @Domotalk (63,513 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/25237/ (1,305 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Analyst, Business Analyst - **Top Industries:** Computer Software, Marketing and Advertising - **Company Size:** 49% Mid-Market, 29% Enterprise #### What Are Domo's Pros and Cons? **Pros:** - Ease of Use (247 reviews) - Data Visualization (116 reviews) - Intuitive (95 reviews) - Easy Integrations (92 reviews) - Integrations (88 reviews) **Cons:** - Learning Curve (66 reviews) - Missing Features (59 reviews) - Data Management Issues (55 reviews) - Expensive (44 reviews) - Complexity (43 reviews) ### What Do G2 Reviewers Say About Domo? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **user-friendly interface** of Domo, enhancing accessibility for both tech-savvy and non-tech-savvy individuals. - Users value the **user-friendly data visualization** features of Domo, enabling effortless graph creation and real-time tracking. - Users highlight Domo's **intuitive design** , making data management accessible for all, even non-tech-savvy individuals. - Users appreciate the **easy integrations** in Domo, enabling seamless data handling from multiple sources for informed decisions. - Users value Domo's **seamless integration** with various data sources, enhancing efficiency and collaboration in data management. **Cons:** - Users find the **learning curve steep** , struggling to keep up with updates and manage functionality challenges effectively. - Users express frustration over **missing features** in Domo, such as dynamic columns and a confusing consumption model. - Users face **data management issues** with Domo, citing unreliable connectors and challenges in reporting and functionality. - Users find Domo's pricing to be **unreasonably expensive** , causing dissatisfaction and trust issues over sudden cost increases. - Users find Domo's **complexity** frustrating due to rigid datasets and cumbersome dashboard management. #### What Are Recent G2 Reviews of Domo? **"[Interactive Real-Time Dashboards That Make Complex Data Easy](https://www.g2.com/survey_responses/domo-review-13003120)"** **Rating:** 5.0/5.0 stars *— Anil B.* [Read full review](https://www.g2.com/survey_responses/domo-review-13003120) --- **"[All-in-One Platform for Real-Time Analytics and Dashboards](https://www.g2.com/survey_responses/domo-review-12676104)"** **Rating:** 5.0/5.0 stars *— Anuj T.* [Read full review](https://www.g2.com/survey_responses/domo-review-12676104) --- #### What Are G2 Users Discussing About Domo? - [What is Domo used for?](https://www.g2.com/discussions/what-is-domo-used-for) - 1 comment - [How much does Domo cost?](https://www.g2.com/discussions/how-much-does-domo-cost) - [What is Domo data?](https://www.g2.com/discussions/what-is-domo-data) - [Is Domo any good?](https://www.g2.com/discussions/is-domo-any-good) - [What does Domo software do?](https://www.g2.com/discussions/what-does-domo-software-do) ### 8. [Incorta](https://www.g2.com/products/incorta/reviews) Incorta is the first and only open data delivery platform that enables real-time analysis of live, detailed data across all systems of record—without the need for complex ETL processes. By enabling direct analysis on raw, source-identical data, Incorta provides faster, more accurate insights while removing barriers to exploration. With intuitive low-code/no-code tools, AI-powered querying through Nexus, and prebuilt business data applications, enterprise teams can quickly surface insights, break down technical roadblocks, and make smarter decisions without heavy engineering effort. For more information, please visit www.incorta.com. **Average Rating:** 4.4/5.0 **Total Reviews:** 55 **How Do G2 Users Rate Incorta?** - **Application:** 9.8/10 (Category avg: 8.5/10) - **Managed Service:** 9.6/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 9.4/10 (Category avg: 8.2/10) - **Ease of Admin:** 9.4/10 (Category avg: 8.5/10) **Who Is the Company Behind Incorta?** - **Seller:** [Incorta](https://www.g2.com/sellers/incorta) - **Company Website:** https://www.incorta.com/ - **Year Founded:** 2013 - **HQ Location:** San Mateo, CA - **LinkedIn® Page:** https://www.linkedin.com/company/incorta/ (348 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 56% Enterprise, 29% Mid-Market #### What Are Incorta's Pros and Cons? **Pros:** - Data Integration (1 reviews) - Easy Integrations (1 reviews) - Integrations (1 reviews) **Cons:** - Bugs (1 reviews) ### What Do G2 Reviewers Say About Incorta? *AI-generated summary from verified user reviews* **Pros:** - Users value the **ease of integration** with various data sources and types, simplifying their data management processes. - Users find the **easy integrations** with various data sources and types extremely beneficial for their workflows. - Users value the **ease of integration** with various data sources and types, enhancing their data analysis capabilities. **Cons:** - Users experience **bugs** with the local data agent not supporting the latest JRE builds, causing functionality issues. #### What Are Recent G2 Reviews of Incorta? **"[Great platformand support](https://www.g2.com/survey_responses/incorta-review-10853785)"** **Rating:** 5.0/5.0 stars *— Jeff W.* [Read full review](https://www.g2.com/survey_responses/incorta-review-10853785) --- **"[Facilitating presentation and information access](https://www.g2.com/survey_responses/incorta-review-9467627)"** **Rating:** 5.0/5.0 stars *— Elsayed H.* [Read full review](https://www.g2.com/survey_responses/incorta-review-9467627) --- #### What Are G2 Users Discussing About Incorta? - [Is Incorta a data warehouse?](https://www.g2.com/discussions/is-incorta-a-data-warehouse) - [What is Incorta?](https://www.g2.com/discussions/what-is-incorta) - [What do you know about Incorta?](https://www.g2.com/discussions/what-do-you-know-about-incorta) - [What is Incorta used for?](https://www.g2.com/discussions/what-is-incorta-used-for) - 1 comment ### 9. [SAP HANA Cloud](https://www.g2.com/products/sap-hana-cloud-2025-10-01/reviews) SAP HANA Cloud is a modern database-as-a-service (DBaaS) powering the next generation of intelligent data applications. SAP HANA Cloud offers a competitive edge by incorporating advanced machine learning and predictive tools grounded in modern data science. Its powerful in-memory performance safeguards efficient data processing. By securely storing vast amounts of data with its integrated multitier storage and handling various types on a single copy in its native multi-model database, SAP HANA Cloud simplifies data management and connects to other data sources. The seamless integration of these capabilities in a reliable, unified foundation makes it easier for developers to build high-demand intelligent data apps. **Average Rating:** 4.3/5.0 **Total Reviews:** 521 **How Do G2 Users Rate SAP HANA Cloud?** - **Application:** 10.0/10 (Category avg: 8.5/10) - **Managed Service:** 9.4/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 10.0/10 (Category avg: 8.2/10) - **Ease of Admin:** 7.9/10 (Category avg: 8.5/10) **Who Is the Company Behind SAP HANA Cloud?** - **Seller:** [SAP](https://www.g2.com/sellers/sap) - **Company Website:** https://www.sap.com/ - **Year Founded:** 1972 - **HQ Location:** Walldorf - **Twitter:** @SAP (297,052 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/sap/ (141,955 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Consultant, SAP Consultant - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 61% Enterprise, 26% Mid-Market #### What Are SAP HANA Cloud's Pros and Cons? **Pros:** - Ease of Use (53 reviews) - Easy Integrations (39 reviews) - Integrations (38 reviews) - Speed (38 reviews) - Scalability (35 reviews) **Cons:** - Complexity (32 reviews) - Expensive (30 reviews) - Learning Curve (29 reviews) - Difficult Learning (27 reviews) - Complex Setup (20 reviews) ### What Do G2 Reviewers Say About SAP HANA Cloud? *AI-generated summary from verified user reviews* **Pros:** - Users find SAP HANA Cloud to be **highly user-friendly** , greatly enhancing workflow efficiency and real-time data management. - Users value the **easy integrations** of SAP HANA Cloud, enhancing data management and streamlining reporting processes seamlessly. - Users commend the **integration capabilities** of SAP HANA Cloud, facilitating seamless connections with SAP and third-party solutions. - Users value the **exceptional real-time performance** of SAP HANA Cloud, enhancing usability and decision-making speed. - Users appreciate the **exceptional scalability** of SAP HANA Cloud, effortlessly managing large datasets while optimizing performance. **Cons:** - Users often find the **complexity of setup and learning** to be a barrier when adopting SAP HANA Cloud. - Users find SAP HANA Cloud **expensive** due to high costs that escalate with usage and complex pricing structures. - Users note a **steep learning curve** with SAP HANA Cloud, making it challenging for newcomers to navigate effectively. - Users find the **learning curve difficult** , particularly for those new to SAP, impacting their ability to utilize the platform. - Users often face **complex setup** challenges with SAP HANA Cloud, making initial configurations difficult for specific applications. #### What Are Recent G2 Reviews of SAP HANA Cloud? **"[Blazing-Fast In-Memory Performance with Seamless SAP Integration](https://www.g2.com/survey_responses/sap-hana-cloud-review-12419032)"** **Rating:** 4.5/5.0 stars *— Dharamveer p.* [Read full review](https://www.g2.com/survey_responses/sap-hana-cloud-review-12419032) --- **"[Efficient Transactions, But Time-Intensive Setup](https://www.g2.com/survey_responses/sap-hana-cloud-review-12983922)"** **Rating:** 4.0/5.0 stars *— Sumeet R.* [Read full review](https://www.g2.com/survey_responses/sap-hana-cloud-review-12983922) --- ### 10. [Box Skills](https://www.g2.com/products/box-skills/reviews) Box Skills is a framework that applies best-of-breed AI technologies from leading providers to your content in Box, creating structure and extracting insights from your data at scale. **Average Rating:** 4.2/5.0 **Total Reviews:** 13 **How Do G2 Users Rate Box Skills?** - **Ease of Admin:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Box Skills?** - **Seller:** [Box](https://www.g2.com/sellers/box) - **Year Founded:** 1998 - **HQ Location:** Redwood City, CA - **Twitter:** @Box (78,537 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/box/ (4,311 employees on LinkedIn®) - **Ownership:** NYSE:BOX **Who Uses This Product?** - **Company Size:** 54% Mid-Market, 31% Small-Business #### What Are Recent G2 Reviews of Box Skills? **"[Box review of usage](https://www.g2.com/survey_responses/box-skills-review-3838623)"** **Rating:** 4.5/5.0 stars *— Srijith S.* [Read full review](https://www.g2.com/survey_responses/box-skills-review-3838623) --- **"[Box Skills is the great solution to track unlimited data and documents. ](https://www.g2.com/survey_responses/box-skills-review-3777554)"** **Rating:** 5.0/5.0 stars *— Jessica G.* [Read full review](https://www.g2.com/survey_responses/box-skills-review-3777554) --- ### 11. [Domino Enterprise AI Platform](https://www.g2.com/products/domino-enterprise-ai-platform/reviews) Domino powers model-driven businesses with its leading Enterprise AI platform that accelerates the development and deployment of data science work while increasing collaboration and governance. More than 20 percent of the Fortune 100 count on Domino to help scale data science, turning it into a competitive advantage. Founded in 2013, Domino is backed by Sequoia Capital and other leading investors. **Average Rating:** 4.3/5.0 **Total Reviews:** 28 **How Do G2 Users Rate Domino Enterprise AI Platform?** - **Application:** 8.5/10 (Category avg: 8.5/10) - **Managed Service:** 8.2/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.2/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.1/10 (Category avg: 8.5/10) **Who Is the Company Behind Domino Enterprise AI Platform?** - **Seller:** [Domino Data Lab](https://www.g2.com/sellers/domino-data-lab) - **Year Founded:** 2013 - **HQ Location:** San Francisco, CA - **Twitter:** @DominoDataLab (7,974 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/3542130/ (257 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 46% Enterprise, 39% Small-Business #### What Are Domino Enterprise AI Platform's Pros and Cons? **Pros:** - Ease of Use (4 reviews) - Easy Integrations (2 reviews) - Integrations (2 reviews) - Training Efficiency (2 reviews) - AI Capabilities (1 reviews) **Cons:** - Cost (1 reviews) - Difficult Setup (1 reviews) - Expensive (1 reviews) - Lack of Guidance (1 reviews) - Missing Features (1 reviews) ### What Do G2 Reviewers Say About Domino Enterprise AI Platform? *AI-generated summary from verified user reviews* **Pros:** - Users value the **exceptional ease of use** of the Domino Enterprise AI Platform, streamlining the AI lifecycle significantly. - Users appreciate the **easy integrations** of Domino Enterprise AI Platform, enhancing flexibility with cloud environments like AWS and Azure. - Users value the **seamless integration** with cloud platforms, enhancing operational efficiency and project collaboration in AI workflows. - Users appreciate the **exceptional training efficiency** of Domino, streamlining the AI lifecycle and enhancing project handoff. - Users value how Domino's platform simplifies the **AI lifecycle management** , enhancing collaboration and operational efficiency. **Cons:** - Users feel the **pricing is on the higher side** , which impacts overall satisfaction with the Domino Enterprise AI Platform. - Users find the **difficult setup** of the Domino Enterprise AI Platform hampers efficiency and slows down access to plugins. - Users feel that the **pricing is on the higher side** , making the Domino Enterprise AI Platform less accessible. - Users feel the platform lacks **guidance for beginners** , making it challenging to navigate effectively. - Users note the **missing features** like easy coding IDE and CV video task support on Domino Enterprise AI Platform. #### What Are Recent G2 Reviews of Domino Enterprise AI Platform? **"[It was an pleasure experience to use Domino as non code AI platform for some of my Automate Job.](https://www.g2.com/survey_responses/domino-enterprise-ai-platform-review-10945668)"** **Rating:** 5.0/5.0 stars *— Shivesh R.* [Read full review](https://www.g2.com/survey_responses/domino-enterprise-ai-platform-review-10945668) --- **"[My thoughts on working with Domino Enterprise AI Platform](https://www.g2.com/survey_responses/domino-enterprise-ai-platform-review-10945085)"** **Rating:** 5.0/5.0 stars *— Swapna D.* [Read full review](https://www.g2.com/survey_responses/domino-enterprise-ai-platform-review-10945085) --- #### What Are G2 Users Discussing About Domino Enterprise AI Platform? - [What is Domino data?](https://www.g2.com/discussions/domino-what-is-domino-data-23fad6ef-f30e-4b45-bac4-9ebf7203f1d6) - [What is Domino data?](https://www.g2.com/discussions/domino-what-is-domino-data-52cde329-cdf5-4d2b-af69-32666b2b6a3e) - [What is Domino data?](https://www.g2.com/discussions/domino-what-is-domino-data) - [What is Domino data?](https://www.g2.com/discussions/what-is-domino-data) - [What is Domino Python?](https://www.g2.com/discussions/domino-what-is-domino-python) - 1 comment ### 12. [Infosys Nia](https://www.g2.com/products/infosys-nia/reviews) Infosys Nia is a knowledge-based AI platform that brings machine learning together with the deep knowledge of an organization to drive automation and innovation and enables businesses to continuously reinvent their system landscapes. **Average Rating:** 4.3/5.0 **Total Reviews:** 12 **How Do G2 Users Rate Infosys Nia?** - **Application:** 8.0/10 (Category avg: 8.5/10) - **Managed Service:** 8.7/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 9.0/10 (Category avg: 8.2/10) - **Ease of Admin:** 7.5/10 (Category avg: 8.5/10) **Who Is the Company Behind Infosys Nia?** - **Seller:** [Infosys](https://www.g2.com/sellers/infosys) - **Year Founded:** 1981 - **HQ Location:** Bangalore, Karnataka - **Twitter:** @Infosys (517,883 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/infosys (369,085 employees on LinkedIn®) - **Ownership:** NSE **Who Uses This Product?** - **Company Size:** 50% Enterprise, 25% Mid-Market #### What Are Recent G2 Reviews of Infosys Nia? **"[Pre-built connectors are time-saver](https://www.g2.com/survey_responses/infosys-nia-review-9767248)"** **Rating:** 4.5/5.0 stars *— Hoang D.* [Read full review](https://www.g2.com/survey_responses/infosys-nia-review-9767248) --- **"[Good end-to-end AI platform for enterprises](https://www.g2.com/survey_responses/infosys-nia-review-9807316)"** **Rating:** 4.0/5.0 stars *— Verified User in Computer Software* [Read full review](https://www.g2.com/survey_responses/infosys-nia-review-9807316) --- ### 13. [Dataloop](https://www.g2.com/products/dataloop-dataloop/reviews) Dataloop is a cutting-edge AI Development Platform that's transforming the way organizations build AI applications. Our platform is meticulously crafted to cater to developers at the heart of the AI development process, making it simpler and more intuitive to work with data and AI models. Our comprehensive solution spans the full AI development lifecycle, offering tools and functionalities that streamline data management, annotation, model selection, and deployment. Dataloop's platform is built with a focus on collaboration, allowing developers, data scientists, and engineers to work together seamlessly, breaking down traditional silos and fostering innovation. Key features include an intuitive drag-and-drop interface for constructing data pipelines, a vast library of pre-built AI elements and models, and robust data curation and annotation capabilities. These features are designed to empower developers to rapidly prototype, iterate, and deploy AI solutions, keeping pace with the fast-evolving demands of the market. Dataloop is committed to advancing AI development by providing a developer-centric platform that addresses the complexities and challenges of AI and data management. Our vision is to democratize AI development, enabling every organization to harness the power of AI and drive forward their innovative solutions. **Average Rating:** 4.4/5.0 **Total Reviews:** 87 **How Do G2 Users Rate Dataloop?** - **Application:** 8.7/10 (Category avg: 8.5/10) - **Managed Service:** 8.6/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.6/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.8/10 (Category avg: 8.5/10) **Who Is the Company Behind Dataloop?** - **Seller:** [Dataloop](https://www.g2.com/sellers/dataloop) - **Year Founded:** 2017 - **HQ Location:** Herzliya, IL - **LinkedIn® Page:** https://www.linkedin.com/company/dataloop (52 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 39% Mid-Market, 33% Small-Business #### What Are Dataloop's Pros and Cons? **Pros:** - Ease of Use (4 reviews) - Annotation Efficiency (2 reviews) - Annotation Tools (2 reviews) - User Interface (2 reviews) - Easy Integrations (1 reviews) **Cons:** - Complexity (1 reviews) - Confusing Syntax (1 reviews) - Difficult Navigation (1 reviews) - Lack of Communication (1 reviews) - Lack of Guidance (1 reviews) ### What Do G2 Reviewers Say About Dataloop? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **ease of use** of Dataloop, highlighting its intuitive navigation and developer-friendly features. - Users value the **annotation efficiency** of Dataloop, praising its simplicity and ease of use across various annotation types. - Users value the **easy annotation** features of Dataloop, enjoying its simplistic interface and diverse annotation types. - Users appreciate the **simple and easy-to-navigate interface** of Dataloop, enhancing their experience with diverse annotation tools. - Users appreciate the **easy integrations** of Dataloop, seamlessly fitting into their existing workflows. **Cons:** - Users find the **complexity of the new UI** confusing, impacting their overall experience with Dataloop. - Users find the **confusing syntax** of Dataloop challenging, impacting their overall user experience significantly. - Users find the **difficult navigation** after the UI change confusing and frustrating while using Dataloop. - Users express a need for improved **communication within the community** to enhance their overall Dataloop experience. - Users feel the **lack of guidance** in Dataloop hinders first-time users from navigating the platform effectively. #### What Are Recent G2 Reviews of Dataloop? **"[I have had a smooth and convenient time every day I am working on Dataloop](https://www.g2.com/survey_responses/dataloop-review-9624539)"** **Rating:** 5.0/5.0 stars *— Mzamil J.* [Read full review](https://www.g2.com/survey_responses/dataloop-review-9624539) --- **"[A journey into Data workflow with Dataloop.](https://www.g2.com/survey_responses/dataloop-review-9633025)"** **Rating:** 4.0/5.0 stars *— Dennis R.* [Read full review](https://www.g2.com/survey_responses/dataloop-review-9633025) --- #### What Are G2 Users Discussing About Dataloop? - [What are data annotations?](https://www.g2.com/discussions/dataloop-what-are-data-annotations) - 1 comment - [What are data annotations?](https://www.g2.com/discussions/what-are-data-annotations) - 1 comment - [Is Dataloop free?](https://www.g2.com/discussions/dataloop-is-dataloop-free) - 1 comment - [Is Dataloop free?](https://www.g2.com/discussions/is-dataloop-free) - 1 comment - [What are the industries that Dataloop supports?](https://www.g2.com/discussions/dataloop-what-are-the-industries-that-dataloop-supports) - 1 comment ### 14. [TIMi](https://www.g2.com/products/timi/reviews) TIMi is the most efficient Data Science and Data Processing Platform. Since 2007, we have been creating and improving the most powerful framework to push the barriers of analytics, predictive analytics, AI and Big Data, while offering a helpful, fast and friendly environment. The TIMi Suite consists of four tools: 1. Anatella (Analytical ETL, Data Prep & Big Data), 2. Modeler (Auto-ML / Automated Predictive Modelling / Automated-AI), 3. StarDust (3D Segmentation) 4. Kibella (BI Dashboarding solution). TIMi dominates the Data Science market: In the "Summer 2022 - Momentum Report” from G2, in the “Data Science” category, TIMi has the #1 rank: TIMi is the Data Science solution with both the highest market growth and the highest customer-satisfaction! More about this subject here: https://timi.eu/blog/timi-the-number-one-data-science-platform/ **Average Rating:** 4.8/5.0 **Total Reviews:** 50 **How Do G2 Users Rate TIMi?** - **Application:** 8.7/10 (Category avg: 8.5/10) - **Managed Service:** 8.6/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 9.3/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.0/10 (Category avg: 8.5/10) **Who Is the Company Behind TIMi?** - **Seller:** [TIMi SPRL](https://www.g2.com/sellers/timi-sprl) - **Year Founded:** 2007 - **HQ Location:** Brussels - **Twitter:** @TIMiSuite (3,532 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/timisuite/ (86 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Information Technology and Services, Banking - **Company Size:** 40% Small-Business, 32% Enterprise #### What Are TIMi's Pros and Cons? **Pros:** - Customer Support (2 reviews) - Ease of Use (2 reviews) - Features (2 reviews) - Automation (1 reviews) - Charting Features (1 reviews) ### What Do G2 Reviewers Say About TIMi? *AI-generated summary from verified user reviews* **Pros:** - Users commend the **quick and helpful customer support** from the TIMi team, enhancing their overall experience significantly. - Users find TIMi **easy to use** , with intuitive learning, quick installs, and excellent customer support enhancing their experience. - Users praise TIMi for its **user-friendly interface and exceptional support** , enhancing data transformation and analysis efficiency. - Users value the **automation capabilities** of TIMi, enabling quick and efficient data transformations and analysis. - Users highlight the **intuitive and fast charting features** of TIMi, simplifying data transformation and analysis. #### What Are Recent G2 Reviews of TIMi? **"[Effortless ETL with Potential for ML Growth](https://www.g2.com/survey_responses/timi-review-12545228)"** **Rating:** 4.0/5.0 stars *— Zainab Y.* [Read full review](https://www.g2.com/survey_responses/timi-review-12545228) --- **"[TIMi and Anatella: fast, scalable, and efficient ML pipeline for very large volumes](https://www.g2.com/survey_responses/timi-review-12712058)"** **Rating:** 5.0/5.0 stars *— Hugo D.* [Read full review](https://www.g2.com/survey_responses/timi-review-12712058) --- #### What Are G2 Users Discussing About TIMi? - [What is TIMi Suite used for?](https://www.g2.com/discussions/what-is-timi-suite-used-for) - 1 comment ### 15. [Plotly Dash Enterprise](https://www.g2.com/products/plotly-dash-enterprise/reviews) Dash is the trusted solution for operationalizing Python models, allowing data science teams to focus on data and models, while still producing and deploying enterprise-ready apps. What would typically require a team of back-end developers, front-end developers and IT can all be done with Dash. It enables data science teams to build, design, deploy, and securely manage data-driven applications that align with your business goals. Companies can deliver on their data, analytic, and AI initiatives quickly and effectively -- no JavaScript, CSS, CronJobs or DevOps required. **Average Rating:** 4.8/5.0 **Total Reviews:** 36 **How Do G2 Users Rate Plotly Dash Enterprise?** - **Ease of Admin:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Plotly Dash Enterprise?** - **Seller:** [Plotly](https://www.g2.com/sellers/plotly) - **Year Founded:** 2013 - **HQ Location:** Montréal, CA - **Twitter:** @plotlygraphs (41,308 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/3327684/ (106 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Computer Software - **Company Size:** 47% Small-Business, 31% Enterprise #### What Are Plotly Dash Enterprise's Pros and Cons? **Pros:** - Charting Features (1 reviews) - Coding Ease (1 reviews) - Customer Support (1 reviews) - Dashboard Management (1 reviews) - Data Visualization (1 reviews) ### What Do G2 Reviewers Say About Plotly Dash Enterprise? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **easy-to-use charting features** of Plotly Dash Enterprise, enabling beautiful visualizations effortlessly. - Users appreciate the **coding ease** of Plotly Dash Enterprise, enjoying intuitive drag-and-drop features for visualization. - Users appreciate the **excellent customer support** provided by Plotly Dash Enterprise, enhancing their overall experience and satisfaction. - Users appreciate the **easy dashboard management** in Plotly Dash Enterprise, simplifying the creation of beautiful visualizations. - Users love the **ease of data visualization** in Plotly Dash Enterprise, creating charts effortlessly with drag-and-drop features. #### What Are Recent G2 Reviews of Plotly Dash Enterprise? **"[Its the perfect Data Management](https://www.g2.com/survey_responses/plotly-dash-enterprise-review-10698270)"** **Rating:** 4.5/5.0 stars *— Maria F.* [Read full review](https://www.g2.com/survey_responses/plotly-dash-enterprise-review-10698270) --- **"[Dash Dominance in Creating Interactive Web Apps for Your Data Insights](https://www.g2.com/survey_responses/plotly-dash-enterprise-review-8889756)"** **Rating:** 5.0/5.0 stars *— Uttam M.* [Read full review](https://www.g2.com/survey_responses/plotly-dash-enterprise-review-8889756) --- #### What Are G2 Users Discussing About Plotly Dash Enterprise? - [What are 3 benefits of a dashboard?](https://www.g2.com/discussions/dash-what-are-3-benefits-of-a-dashboard) - [What is the best dashboard software?](https://www.g2.com/discussions/dash-what-is-the-best-dashboard-software) - [How good is Dash?](https://www.g2.com/discussions/how-good-is-dash) - [What is the function of a dashboard?](https://www.g2.com/discussions/what-is-the-function-of-a-dashboard) ### 16. [Google Cloud AI Hub](https://www.g2.com/products/google-cloud-ai-hub/reviews) Google Cloud’s Artificial Intelligence (AI) Hub is a catalog of plug-and-play AI components, including end-to-end AI pipelines and out-of-the-box algorithms. **Average Rating:** 4.3/5.0 **Total Reviews:** 12 **How Do G2 Users Rate Google Cloud AI Hub?** - **Application:** 8.5/10 (Category avg: 8.5/10) - **Managed Service:** 8.3/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 7.6/10 (Category avg: 8.2/10) - **Ease of Admin:** 7.5/10 (Category avg: 8.5/10) **Who Is the Company Behind Google Cloud AI Hub?** - **Seller:** [Google](https://www.g2.com/sellers/google) - **Year Founded:** 1998 - **HQ Location:** Mountain View, CA - **Twitter:** @google (31,899,995 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (341,888 employees on LinkedIn®) - **Ownership:** NASDAQ:GOOG **Who Uses This Product?** - **Company Size:** 42% Small-Business, 33% Enterprise #### What Are Recent G2 Reviews of Google Cloud AI Hub? **"[Simplifying AI with Google Cloud AI Hub](https://www.g2.com/survey_responses/google-cloud-ai-hub-review-8445224)"** **Rating:** 4.0/5.0 stars *— Deepak P.* [Read full review](https://www.g2.com/survey_responses/google-cloud-ai-hub-review-8445224) --- **"[Good experience](https://www.g2.com/survey_responses/google-cloud-ai-hub-review-8448997)"** **Rating:** 5.0/5.0 stars *— Majd A.* [Read full review](https://www.g2.com/survey_responses/google-cloud-ai-hub-review-8448997) --- #### What Are G2 Users Discussing About Google Cloud AI Hub? - [What is Google Cloud AI Hub used for?](https://www.g2.com/discussions/what-is-google-cloud-ai-hub-used-for) - 1 comment ### 17. [TrueFoundry](https://www.g2.com/products/truefoundry/reviews) TrueFoundry provides an enterprise-grade AI Gateway that encompasses an LLM Gateway, MCP Gateway, and Agent Gateway, enabling enterprises to securely connect, observe, and govern access to models, tools, guardrails, and agents from a single control plane. The AI Gateway enables agentic workloads that are secure, efficient, and future-safe through unified and composable connections across providers. Beyond the gateway layer, TrueFoundry enables organizations to deploy and train custom LLMs on GPUs, host MCP servers, and run custom agents—all through a Kubernetes-native interface. It supports on-premise and VPC installations for both AI Gateway and deployment environments. TrueFoundry ensures enterprise-grade compliance with SOC 2, HIPAA, and ITAR standards. With built-in autoscaling, caching, and resource optimization, TrueFoundry empowers organizations to build, deploy, and govern AI systems securely, efficiently, and on a future-safe stack. Visit www.truefoundry.com to learn more **Average Rating:** 4.6/5.0 **Total Reviews:** 54 **How Do G2 Users Rate TrueFoundry?** - **Application:** 8.1/10 (Category avg: 8.5/10) - **Managed Service:** 8.6/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.1/10 (Category avg: 8.2/10) - **Ease of Admin:** 9.0/10 (Category avg: 8.5/10) **Who Is the Company Behind TrueFoundry?** - **Seller:** [TrueFoundry](https://www.g2.com/sellers/truefoundry) - **Company Website:** https://www.truefoundry.com/ - **Year Founded:** 2021 - **HQ Location:** San Francisco, California - **LinkedIn® Page:** https://www.linkedin.com/company/truefoundry/about (108 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 49% Mid-Market, 36% Small-Business #### What Are TrueFoundry's Pros and Cons? **Pros:** - Ease of Use (17 reviews) - User Interface (12 reviews) - Customer Support (11 reviews) - Deployment Ease (11 reviews) - Easy Integrations (8 reviews) **Cons:** - Missing Features (5 reviews) - Complexity (2 reviews) - Complexity Issues (2 reviews) - Deployment Issues (2 reviews) - Difficult Setup (2 reviews) ### What Do G2 Reviewers Say About TrueFoundry? *AI-generated summary from verified user reviews* **Pros:** - Users enjoy the **ease of use** of TrueFoundry, simplifying deployments and enhancing productivity with a user-friendly interface. - Users value the **easy-to-use UI** of TrueFoundry, enhancing seamless deployments and monitoring capabilities effortlessly. - Users appreciate the **exceptional customer support** from TrueFoundry, with quick responses to queries and helpful assistance. - Users appreciate the **streamlined deployment process** of TrueFoundry, making model deployment effortless and efficient. - Users appreciate the **easy integrations** of TrueFoundry, enabling seamless model deployment and flexible management across teams. **Cons:** - Users desire **additional features** like no code/low code options and enhanced dashboard capabilities in TrueFoundry. - Users find the **complexity** of TrueFoundry challenging, requiring significant learning for advanced features and custom setups. - Users find the **learning complexity** of TrueFoundry challenging, especially without prior cloud or Kubernetes knowledge. - Users note **deployment issues** with Hugging Face models on TrueFoundry, wishing for more finetuning options. - Users find the **difficult setup** of TrueFoundry challenging, especially without prior cloud or Kubernetes knowledge. #### What Are Recent G2 Reviews of TrueFoundry? **"[Exceptional Prototyping Speed with One-Click Deployments and Branch-Based Iteration](https://www.g2.com/survey_responses/truefoundry-review-12739746)"** **Rating:** 5.0/5.0 stars *— Tara B.* [Read full review](https://www.g2.com/survey_responses/truefoundry-review-12739746) --- **"[Ensures Business Continuity with Exceptional Support](https://www.g2.com/survey_responses/truefoundry-review-12609940)"** **Rating:** 5.0/5.0 stars *— Shobhit V.* [Read full review](https://www.g2.com/survey_responses/truefoundry-review-12609940) --- ### 18. [KNIME](https://www.g2.com/products/knime-analytics-platform/reviews) KNIME helps everybody make sense of data. Its free and open source KNIME Analytics Platform enables anyone — whether they come from a business, technical or data background — to intuitively work with data, every day. KNIME Business Hub is the commercial complement to KNIME Analytics Platform and enables users to collaborate on data science and share insights across the organization. Together, the products support the complete data science lifecycle, allowing teams at all levels of analytics readiness to support the operationalization of data and to build a scalable data science practice. **Average Rating:** 4.5/5.0 **Total Reviews:** 89 **How Do G2 Users Rate KNIME?** - **Application:** 8.5/10 (Category avg: 8.5/10) - **Managed Service:** 7.6/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.1/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.0/10 (Category avg: 8.5/10) **Who Is the Company Behind KNIME?** - **Seller:** [KNIME](https://www.g2.com/sellers/knime) - **Company Website:** https://knime.com - **Year Founded:** 2008 - **HQ Location:** Zurich, Switzerland - **Twitter:** @knime (7,998 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/692207?trk=tyah&trkInfo=clickedVertical%3Acompany%2CclickedEntityId%3A692207%2Cidx%3A2-1-4%2CtarId%3A1454002156993%2Ctas%3Aknime (241 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Information Technology and Services, Higher Education - **Company Size:** 43% Enterprise, 34% Mid-Market #### What Are KNIME's Pros and Cons? **Pros:** - Ease of Use (7 reviews) - Coding Ease (4 reviews) - Ease of Learning (4 reviews) - Learning (4 reviews) - Data Visualization (3 reviews) **Cons:** - Learning Difficulty (3 reviews) - Memory Usage (3 reviews) - Storage Limitations (3 reviews) - Data Management Issues (2 reviews) - Insufficient Learning Resources (2 reviews) ### What Do G2 Reviewers Say About KNIME? *AI-generated summary from verified user reviews* **Pros:** - Users find KNIME's **ease of use** remarkable, allowing non-technical individuals to create workflows effortlessly. - Users find KNIME's **coding ease** exceptional, allowing non-technical users to build workflows effortlessly. - Users find KNIME **easy to learn** , enabling quick delivery of workflows without needing extensive technical knowledge. - Users praise KNIME for its **easy learning curve** , enabling both beginners and experts to create powerful data solutions. - Users praise the **ease of data visualization** in KNIME, making it accessible for both beginners and experienced analysts. **Cons:** - Users face a **challenging learning curve** with KNIME, especially if they lack data science or visual programming experience. - Users experience **high memory consumption** issues with KNIME, causing slow performance and challenges in handling large files. - Users experience **storage limitations** due to high memory consumption, affecting performance with large files. - Users find **data management issues** significant, citing difficulties with file handling and reading from various databases. - Users find **insufficient learning resources** for KNIME, which hinders their ability to fully utilize the software. #### What Are Recent G2 Reviews of KNIME? **"[KNIME’s Free No-Code Drag-and-Drop Analytics, from Descriptive to Agentic AI](https://www.g2.com/survey_responses/knime-review-12992618)"** **Rating:** 5.0/5.0 stars *— Guylaine B.* [Read full review](https://www.g2.com/survey_responses/knime-review-12992618) --- **"[KNIME’s Visual Workflows - One of the best tool for Auditing, Accounting & Finance Professionals](https://www.g2.com/survey_responses/knime-review-12976842)"** **Rating:** 5.0/5.0 stars *— Charm M.* [Read full review](https://www.g2.com/survey_responses/knime-review-12976842) --- #### What Are G2 Users Discussing About KNIME? - [What is KNIME Analytics Platform used for?](https://www.g2.com/discussions/what-is-knime-analytics-platform-used-for) - [Is Knime easy to use?](https://www.g2.com/discussions/is-knime-easy-to-use) - 1 comment - [How do I use Knime Analytics?](https://www.g2.com/discussions/how-do-i-use-knime-analytics) - [Is Knime any good?](https://www.g2.com/discussions/is-knime-any-good) - [What is Knime analytics platform?](https://www.g2.com/discussions/what-is-knime-analytics-platform) ### 19. [DataRobot](https://www.g2.com/products/datarobot/reviews) DataRobot’s enterprise AI platform democratizes data science with end-to-end automation for building, deploying, and managing machine learning models. This platform maximizes business value by delivering AI at scale and continuously optimizing performance over time. The company’s proven combination of cutting edge software and world-class AI implementation, training, and support services, empowers any organization – regardless of size, industry, or resources – to drive better business outcomes with AI. **Average Rating:** 4.4/5.0 **Total Reviews:** 27 **How Do G2 Users Rate DataRobot?** - **Application:** 5.0/10 (Category avg: 8.5/10) - **Managed Service:** 1.7/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 0.0/10 (Category avg: 8.2/10) - **Ease of Admin:** 7.4/10 (Category avg: 8.5/10) **Who Is the Company Behind DataRobot?** - **Seller:** [DataRobot](https://www.g2.com/sellers/datarobot) - **Year Founded:** 2012 - **HQ Location:** Boston, Massachusetts - **Twitter:** @DataRobot (19,225 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/2672915/ (875 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Computer Software - **Company Size:** 54% Small-Business, 29% Enterprise #### What Are Recent G2 Reviews of DataRobot? **"[Incredible tool for quick raw data to insights](https://www.g2.com/survey_responses/datarobot-review-12876763)"** **Rating:** 5.0/5.0 stars *— Verified User in Computer Software* [Read full review](https://www.g2.com/survey_responses/datarobot-review-12876763) --- **"[Fast, Insightful Automated Modeling with DataRobot](https://www.g2.com/survey_responses/datarobot-review-11788506)"** **Rating:** 4.5/5.0 stars *— Verified User in Financial Services* [Read full review](https://www.g2.com/survey_responses/datarobot-review-11788506) --- ### 20. [DagsHub](https://www.g2.com/products/dagshub/reviews) DagsHub is a platform that allows you to easily create high-quality datasets for better model performance A single AI platform to curate vision, audio, and document data - automate labeling workflows, and evaluate models. Enterprises with sensitive data, can run on their own infrastructure on-prem and get a full AI platform. Data curation - create the very best datasets. Data annotation - annotate your vision, audio, and document data. Auto labeling - automate your annotation flow with pre-built templates and active learning. Data versioning - version your datasets for reproducibility. Experiment tracking - track your experiment progress, understand trends, and compare results. Model registry - manage your models and deployments in one place. The top data scientists build AI with DagsHub including teams at: Google, Harvard Medicine, Beewise, Macso, and Mana.bio **Average Rating:** 4.8/5.0 **Total Reviews:** 14 **How Do G2 Users Rate DagsHub?** - **Application:** 8.8/10 (Category avg: 8.5/10) - **Managed Service:** 8.6/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.8/10 (Category avg: 8.2/10) - **Ease of Admin:** 9.2/10 (Category avg: 8.5/10) **Who Is the Company Behind DagsHub?** - **Seller:** [DagsHub](https://www.g2.com/sellers/dagshub) - **HQ Location:** San Francisco, US - **LinkedIn® Page:** https://www.linkedin.com/company/dagshub (12 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Computer Software - **Company Size:** 50% Small-Business, 43% Mid-Market #### What Are DagsHub's Pros and Cons? **Pros:** - Data Management (12 reviews) - Model Management (12 reviews) - Collaboration (11 reviews) - Features (10 reviews) - Integrated Platform (10 reviews) **Cons:** - Limited Functionality (2 reviews) - Error Handling (1 reviews) - Expensive (1 reviews) - Limited Customization (1 reviews) - Limited Free Access (1 reviews) ### What Do G2 Reviewers Say About DagsHub? *AI-generated summary from verified user reviews* **Pros:** - Users value the **effective data management** capabilities of DagsHub, enhancing reproducibility and collaboration in ML projects. - Users value the **integrated management of data, code, and experiments** in DagsHub, enhancing productivity and collaboration. - Users appreciate the **seamless collaboration** features of DagsHub, enhancing productivity and efficiency in data management and experimentation. - Users appreciate the **integration of data and experiments** in DagsHub, enhancing reproducibility and collaboration in ML projects. - Users value the **integrated platform** of DagsHub for simplifying data management and enhancing collaboration in ML projects. **Cons:** - Users find the **limited functionality** of DagsHub restrictive, especially regarding collaboration on the free plan. - Users experience **error handling issues** with DAGsHub, particularly when pushing files and loading projects. - Users find DagsHub to be **expensive** , especially with strict limitations on the free plan for larger teams. - Users are frustrated with the **limited customization** options on DagsHub, especially regarding team size restrictions. - Users find the **strict limitations of the free plan** frustrating, affecting team collaboration and accessibility. #### What Are Recent G2 Reviews of DagsHub? **"[Simplifies LLM Dataset Versioning and Experiment Tracking](https://www.g2.com/survey_responses/dagshub-review-11144209)"** **Rating:** 5.0/5.0 stars *— Gourav B.* [Read full review](https://www.g2.com/survey_responses/dagshub-review-11144209) --- **"[Reliable Infrastructure for LLM Data and Model Iteration](https://www.g2.com/survey_responses/dagshub-review-11087413)"** **Rating:** 5.0/5.0 stars *— Ignacio P.* [Read full review](https://www.g2.com/survey_responses/dagshub-review-11087413) --- ### 21. [IBM Cloud Pak for Data](https://www.g2.com/products/ibm-cloud-pak-for-data/reviews) IBM Cloud Pak® for Data is a fully integrated data and AI platform that modernizes how businesses collect, organize and analyze data, forming the foundation to infuse AI across their organization. Running on Red Hat OpenShift and available on any cloud, this unified platform helps companies automate the end-to-end AI lifecycle. The intelligent data fabric in IBM Cloud Pak for Data enables automated distributed queries at scale without data movement; automated discovery and understanding of business-ready data; automated universal privacy and usage policies across the data ecosystem; and optimized model training, accuracy and explainability. View the demo: https://mediacenter.ibm.com/media/1\_je41fqqz. The platform delivers on the below use cases: • Data access and availability – Eliminate data silos and simplify your data landscape to enable faster, cost-effective extraction of value from your data. • Data quality and governance - Apply governance solutions and methodologies to deliver trusted, business data. • Data privacy and security - Fully understand and manage sensitive data with a pervasive privacy framework. • ModelOps - Automate the AI lifecycle and synchronize application and model pipelines to scale AI deployments. • AI governance – Ensure your AI is transparent, compliant and trustworthy with greater visibility into model development, with capabilities such as explainable AI, model risk management and bias detection. • AI for Financial Operations - Automate and integrate planning across your organization, from financial planning & analysis to workforce planning, sales forecasting and supply chain planning. • AI for Customer care - Reduce time to resolution, decrease call volume and increase customer satisfaction. IBM Watson Assistant (WA) can provide AI-powered automated assistance and enable human agents to better handle inquiries. IBM Watson Discovery (WD) complements Watson Assistant and can help unlock insights from complex business content. Discover IBM Cloud Pak for Data Industry Accelerators: https://dataplatform.cloud.ibm.com/gallery?context=cpdaas See a case study: https://mediacenter.ibm.com/media/1\_sr6lx8sz Try at no-cost: http://ibm.biz/dataplatformtrial **Average Rating:** 4.3/5.0 **Total Reviews:** 72 **How Do G2 Users Rate IBM Cloud Pak for Data?** - **Application:** 8.8/10 (Category avg: 8.5/10) - **Managed Service:** 8.5/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 9.2/10 (Category avg: 8.2/10) - **Ease of Admin:** 7.6/10 (Category avg: 8.5/10) **Who Is the Company Behind IBM Cloud Pak for Data?** - **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, Information Technology and Services - **Company Size:** 51% Enterprise, 28% Small-Business #### What Are Recent G2 Reviews of IBM Cloud Pak for Data? **"[Comprehensive solution for data-intensive workflows](https://www.g2.com/survey_responses/ibm-cloud-pak-for-data-review-12967373)"** **Rating:** 4.5/5.0 stars *— Amr a.* [Read full review](https://www.g2.com/survey_responses/ibm-cloud-pak-for-data-review-12967373) --- **"[From Data Silos to Actionable Insights: IBM Cloud Pak for Data Delivers](https://www.g2.com/survey_responses/ibm-cloud-pak-for-data-review-9931702)"** **Rating:** 4.0/5.0 stars *— Yasser A.* [Read full review](https://www.g2.com/survey_responses/ibm-cloud-pak-for-data-review-9931702) --- ### 22. [SAS Enterprise Miner](https://www.g2.com/products/sas-enterprise-miner/reviews) SAS Enterprise Miner is a comprehensive data mining and predictive analytics software designed to streamline the process of developing descriptive and predictive models. It enables users to analyze vast amounts of data efficiently, uncovering patterns and relationships that inform better decision-making. With an intuitive graphical user interface, SAS Enterprise Miner facilitates the entire data mining process, from data preparation to model assessment, making advanced analytics accessible to both technical and non-technical users. Key Features and Functionality: - User-Friendly Interface: An interactive GUI allows users to build process flow diagrams, simplifying the modeling process. - Advanced Data Preparation: Tools for handling missing values, filtering outliers, and performing data transformations enhance data quality. - Diverse Modeling Techniques: Supports a wide range of algorithms, including decision trees, neural networks, and regression models, catering to various analytical needs. - Open Source Integration: Seamless integration with R enables users to perform data transformations and model training within the platform. - High-Performance Capabilities: Incorporates high-performance data mining nodes to boost processing efficiency. - Automated Scoring: Generates score code in multiple languages (SAS, C, Java, PMML) for deployment across various environments. - Model Comparison and Management: Features for comparing multiple models using lift curves and statistical diagnostics to identify the best-performing models. Primary Value and Solutions Provided: SAS Enterprise Miner empowers organizations to harness the full potential of their data by providing a robust platform for developing accurate predictive models. It addresses challenges such as fraud detection, risk minimization, resource demand forecasting, and customer attrition reduction. By automating and simplifying complex data mining tasks, it enables users to make informed, data-driven decisions, ultimately enhancing operational efficiency and competitive advantage. **Average Rating:** 4.2/5.0 **Total Reviews:** 185 **How Do G2 Users Rate SAS Enterprise Miner?** - **Application:** 8.1/10 (Category avg: 8.5/10) - **Managed Service:** 8.1/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.1/10 (Category avg: 8.2/10) - **Ease of Admin:** 7.7/10 (Category avg: 8.5/10) **Who Is the Company Behind SAS Enterprise Miner?** - **Seller:** [SAS Institute Inc.](https://www.g2.com/sellers/sas-institute-inc-df6dde22-a5e5-4913-8b21-4fa0c6c5c7c2) - **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®) - **Phone:** 1-800-727-0025 **Who Uses This Product?** - **Top Industries:** Higher Education, Information Technology and Services - **Company Size:** 60% Enterprise, 28% Mid-Market #### What Are SAS Enterprise Miner's Pros and Cons? **Pros:** - Ease of Installation (1 reviews) - Ease of Use (1 reviews) - Statistical Analysis (1 reviews) **Cons:** - Learning Curve (1 reviews) - Not User-Friendly (1 reviews) - Steep Learning Curve (1 reviews) ### What Do G2 Reviewers Say About SAS Enterprise Miner? *AI-generated summary from verified user reviews* **Pros:** - Users value the **ease of installation** for node-based analysis in SAS Enterprise Miner, enhancing their workflow efficiency. - Users appreciate the **ease of node-based analysis** in SAS Enterprise Miner, enhancing their data exploration experience. - Users praise the **ease of creating node-based analyses** in SAS Enterprise Miner, enhancing usability and efficiency. **Cons:** - Users find the **learning curve steep** for SAS Enterprise Miner, making it difficult for beginners to generate outputs. - Users find SAS Enterprise Miner to be **not user-friendly** , especially when generating output for other SAS products. - Users find the **steep learning curve** of SAS Enterprise Miner challenging, particularly when generating outputs to other SAS products. #### What Are Recent G2 Reviews of SAS Enterprise Miner? **"[Great Decision Trees That Deliver](https://www.g2.com/survey_responses/sas-enterprise-miner-review-12697539)"** **Rating:** 4.5/5.0 stars *— Rohan K.* [Read full review](https://www.g2.com/survey_responses/sas-enterprise-miner-review-12697539) --- **"[Excellent product for statistical analysis](https://www.g2.com/survey_responses/sas-enterprise-miner-review-11134264)"** **Rating:** 5.0/5.0 stars *— bobby x.* [Read full review](https://www.g2.com/survey_responses/sas-enterprise-miner-review-11134264) --- #### What Are G2 Users Discussing About SAS Enterprise Miner? - [Is SAS software dying?](https://www.g2.com/discussions/sas-advanced-analytics-is-sas-software-dying) - [Who uses SAS Enterprise Miner?](https://www.g2.com/discussions/who-uses-sas-enterprise-miner) - [What is SAS software used for?](https://www.g2.com/discussions/sas-advanced-analytics-what-is-sas-software-used-for) - [Is SAS Enterprise Miner useful?](https://www.g2.com/discussions/is-sas-enterprise-miner-useful) - [What is advanced analytics platform?](https://www.g2.com/discussions/what-is-advanced-analytics-platform) - 1 comment ### 23. [Encord](https://www.g2.com/products/encord/reviews) Encord is the universal data layer for AI. The platform helps AI teams train and run their models with the right data - managing, curating, annotating, and aligning data across the full AI lifecycle. Encord works with over 300 leading AI teams, including Woven by Toyota, Zipline, AXA, and Flock Safety. Confidentially build production AI with rich multimodal data. Encord is SOC 2, AICPA SOC, HIPAA, and GDPR compliant. **Average Rating:** 4.8/5.0 **Total Reviews:** 65 **How Do G2 Users Rate Encord?** - **Application:** 9.7/10 (Category avg: 8.5/10) - **Managed Service:** 10.0/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 10.0/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.1/10 (Category avg: 8.5/10) **Who Is the Company Behind Encord?** - **Seller:** [Encord](https://www.g2.com/sellers/encord) - **Year Founded:** 2020 - **HQ Location:** San Francisco, US - **Twitter:** @encord_team (1,014 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/69557125 (183 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Computer Software, Hospital & Health Care - **Company Size:** 51% Small-Business, 40% Mid-Market #### What Are Encord's Pros and Cons? **Pros:** - Customer Support (5 reviews) - Annotation Efficiency (3 reviews) - Annotation Tools (3 reviews) - Efficiency (3 reviews) - Features (3 reviews) **Cons:** - Complex Automation (1 reviews) - Complexity (1 reviews) - Lack of Guidance (1 reviews) ### What Do G2 Reviewers Say About Encord? *AI-generated summary from verified user reviews* **Pros:** - Users commend Encord's **responsive customer support** , valuing their hands-on partnership and flexibility in adapting to needs. - Users praise Encord for its **annotation efficiency** , enabling smooth workflows and saving valuable time in data curation. - Users commend Encord's **intuitive annotation tools** , which streamline workflows and enhance collaboration for training data curation. - Users appreciate the **efficiency** of Encord, highlighted by smooth workflows and quick support for annotation needs. - Users praise Encord for its **exceptional customer support and intuitive features** , streamlining their training data processes effectively. **Cons:** - Users find **custom workflows complex** , though support from the team helps navigate the challenges effectively. - Users find the **rapid feature updates** of Encord challenging, making it difficult to stay current with best practices. - Users find the **lack of guidance** challenging due to frequent feature updates, despite support from the customer success team. #### What Are Recent G2 Reviews of Encord? **"[Strong video labeling platform with excellent support](https://www.g2.com/survey_responses/encord-review-12281672)"** **Rating:** 5.0/5.0 stars *— Angela S.* [Read full review](https://www.g2.com/survey_responses/encord-review-12281672) --- **"[Built for fast model development cycles](https://www.g2.com/survey_responses/encord-review-12219596)"** **Rating:** 5.0/5.0 stars *— Brian E.* [Read full review](https://www.g2.com/survey_responses/encord-review-12219596) --- ### 24. [Explorium](https://www.g2.com/products/explorium/reviews) Explorium is the leading B2B data layer for building high-performance GTM agents. Our seamless API and MCP integrations power agents with premium B2B data, accelerating development, enhancing contextual intelligence, and improving ROI. Backed by years of expertise in B2B data and access to more than 50 data sources, we deliver enterprise-grade data through flexible delivery options. Try it for free at https://www.explorium.ai/sign-up/?utm\_source=g2&utm\_medium\_organic **Average Rating:** 4.6/5.0 **Total Reviews:** 28 **How Do G2 Users Rate Explorium?** - **Application:** 10.0/10 (Category avg: 8.5/10) - **Managed Service:** 10.0/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 10.0/10 (Category avg: 8.2/10) - **Ease of Admin:** 10.0/10 (Category avg: 8.5/10) **Who Is the Company Behind Explorium?** - **Seller:** [Explorium](https://www.g2.com/sellers/explorium) - **HQ Location:** San Mateo, California - **Twitter:** @Explorium_ai (1,347 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/18828451/ (88 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Financial Services, Education Management - **Company Size:** 57% Small-Business, 39% Mid-Market #### What Are Recent G2 Reviews of Explorium? **"[Transformative Data Enrichment & Seamless Integration](https://www.g2.com/survey_responses/explorium-review-12561656)"** **Rating:** 5.0/5.0 stars *— Akash S.* [Read full review](https://www.g2.com/survey_responses/explorium-review-12561656) --- **"[Excellent Data Quality!](https://www.g2.com/survey_responses/explorium-review-12634333)"** **Rating:** 5.0/5.0 stars *— Jacob S.* [Read full review](https://www.g2.com/survey_responses/explorium-review-12634333) --- #### What Are G2 Users Discussing About Explorium? - [What is Explorium used for?](https://www.g2.com/discussions/what-is-explorium-used-for) ### 25. [Red Hat OpenShift Data Science](https://www.g2.com/products/red-hat-openshift-data-science/reviews) Red Hat® OpenShift® AI is a flexible, scalable artificial intelligence (AI) and machine learning (ML) platform that enables enterprises to create and deliver AI-enabled applications at scale across hybrid cloud environments. Built using open source technologies, OpenShift AI provides trusted, operationally consistent capabilities for teams to experiment, serve models, and deliver innovative apps. **Average Rating:** 4.4/5.0 **Total Reviews:** 25 **How Do G2 Users Rate Red Hat OpenShift Data Science?** - **Application:** 8.7/10 (Category avg: 8.5/10) - **Managed Service:** 8.8/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.6/10 (Category avg: 8.2/10) - **Ease of Admin:** 6.7/10 (Category avg: 8.5/10) **Who Is the Company Behind Red Hat OpenShift Data Science?** - **Seller:** [Red Hat](https://www.g2.com/sellers/red-hat) - **Year Founded:** 1993 - **HQ Location:** Raleigh, NC - **Twitter:** @RedHat (300,769 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/3545/ (19,413 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Market Research, Marketing and Advertising - **Company Size:** 44% Mid-Market, 36% Enterprise #### What Are Recent G2 Reviews of Red Hat OpenShift Data Science? **"[Transforming Business Analysis: Containerization for Agile Collaboration](https://www.g2.com/survey_responses/red-hat-openshift-data-science-review-8642746)"** **Rating:** 4.5/5.0 stars *— Adrian Andres J.* [Read full review](https://www.g2.com/survey_responses/red-hat-openshift-data-science-review-8642746) --- **"[Allows you to explore and discover valuable insights](https://www.g2.com/survey_responses/red-hat-openshift-data-science-review-8993201)"** **Rating:** 5.0/5.0 stars *— kelly R.* [Read full review](https://www.g2.com/survey_responses/red-hat-openshift-data-science-review-8993201) --- #### What Are G2 Users Discussing About Red Hat OpenShift Data Science? - [What is Red Hat OpenShift Data Science used for?](https://www.g2.com/discussions/what-is-red-hat-openshift-data-science-used-for) ## 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.