Report:
Magic Quadrant for Data Science and Machine Learning Platforms
How does Gartner define the Data Science and Machine Learning Platforms market in 2025?
Gartner defines a data science and machine learning platform as an integrated set of code-based libraries and low-code tooling. These platforms support the independent use and collaboration among data scientists and their business and IT counterparts, with automation and AI assistance through all stages of the data science life cycle, including business understanding, data access and preparation, model creation and sharing of insights. They also support engineering workflows, including the creation of data, feature, deployment and testing pipelines. The platforms are provided via desktop client or browser with supporting compute instances or as a fully managed cloud offering. Data science and machine learning (DSML) platforms are designed to enable a broad range of users to develop and apply a comprehensive suite of predictive and prescriptive analytical techniques. Leveraging data from distributed sources, code-first and low-code interfaces, and native machine learning and generative AI (GenAI) capabilities, these platforms enhance and automate decision making across an enterprise.
Key Facts for Magic Quadrant for Data Science and Machine Learning Platforms in 2025
- Publication Date: 28 May 2025
- Document ID: G00822217
- Coverage: Global
- Authors: Afraz Jaffri, Maryam Hassanlou
- Core Purpose: Data science and machine learning platforms provide software to build, customize and deploy AI models using various techniques. Data science, machine learning and AI leaders need to select platforms with awareness of AI agents enabling autonomous and interactive workflows and applications.
Strategic Planning Assumptions
- By 2027, 50% of data analysts will be retrained as data scientists, and data scientists will shift to AI engineers
- By 2027, organizations will implement small, task-specific AI models, with usage volume at least three times more than those of general-purpose large language models
How was the Data Science and Machine Learning Platforms market evolved in 2025?
- DSML platforms are increasingly focused on empowering the creation of agentic systems with integrated LLMs and GenAI-driven assistants
- Over 50% of respondents in Gartner's 2024 Analytics and AI Engineering survey report that AI tools for automated insights and natural language queries are being used in AI development
- The strategic value of DSML platforms within enterprises continues to rise, fueled by growing demand for advanced AI solutions
- Composite AI systems that combine predictive and generative models will become a standard methodology for AI development
- DSML platforms need to handle data and operations across multiple environments, including multicloud, hybrid and on-premises infrastructure
- Major cloud providers continue to expand their influence due to comprehensive compute, data and infrastructure offerings needed for DSML development
- Enhanced AI governance and management capabilities, linked across data sources and other assets, are now must-have capabilities
- AutoML remains a foundational feature, now supplemented by GenAI-driven assistants that provide coding support, natural language interaction and workflow creation
What product features are required to be included in this year's evaluation?
- Import or connect to tabular data from data management systems, including databases, data warehouses, and content repositories located on-premises and in the cloud.
- Preparation of data using data transformation tools and packages.
- Code-based development environment.
- Building and evaluation of models using a library of core data science and machine learning techniques, methods, algorithms and processes.
- Collaboration and project management tools to allow multiple users and teams to use the platform.
- Deployment, hosting and serving models in the platform for usage in services and applications.
- Model life cycle management to promote, demote, retrain and retire models.
- Administration and configuration management for user roles, permissions and resource allocation.
What are the common features of top products in the Data Science and Machine Learning Platforms space?
- Platform-generated recommendations for the best way to prepare, integrate and model data, as well as automated creation of machine learning models based on manually selected target prediction.
- Advanced interfaces that facilitate more complex modeling for simulation, optimization and deep-learning-based use cases.
- Custom software development kits (SDKs) that provide more control and flexibility for code-based model development and integration with services and applications.
- Utilize structured and unstructured data sources including text, images, video, audio and geospatial data.
- Low-code interface for model development and AutoML functions suitable for nonexpert data science roles, including business users and domain experts.
- Postdeployment model life cycle management to retrain, retire or adapt models based on detecting and analyzing data, feature and model drift.
- Support for MLOps-based processes and tools that enable machine learning (ML) models to be deployed at scale in different operational environments.
- Functionality for working with GenAI models, such as large language models, through tracking, selection and monitoring of prompts, models and outputs.
- Techniques and tools that increase the transparency, explainability and interpretability of models to understand how and why model outputs are generated.
- Metadata management and cataloging through structured repositories of key assets including data, code, features, models, logs and outputs.
- GPU support for training deep learning and generative AI models.
- Advanced data visualization to enable exploration and investigation of data for hypothesis testing, data validation and use case identification.
- Integrated data lineage and provenance tracking to ensure data integrity and traceability throughout the data science workflow.
- Advanced hyperparameter tuning and optimization tools to enhance model performance and efficiency.
Scope Exclusions
- Platforms not focused on professional data scientists, ML engineers, or AI engineers as primary personas
- Platforms not recommended for predominantly code-centric data science teams
- Revenue below minimum thresholds ($100M, or $50M with 20% growth, or $25M with 40% growth)
- Insufficient geographic presence (less than 10 paying customers in two regions)
- Customer Interest Index score below 55 points
Inclusion Criteria
Vendors must, among other requirements:
- Go-to-market strategy focusing on professional data scientists, ML engineers, or AI engineers
- Platform recommended for use in predominantly code-centric data science teams
- At least $100M calendar year 2024 platform revenue (excluding hardware and services), OR at least $50M with 20% YoY growth, OR at least $25M with 40% YoY growth
- At least 10 paying customers in each of two regions (North America, South America, EMEA, or Asia/Pacific) as of 1 January 2025
- Customer Interest Index score above 55 points
Ability to Execute — Relative Weighting
- Product or Service - High
- Overall Viability - High
- Sales Execution/Pricing - Low
- Market Responsiveness/Record - Medium
- Marketing Execution - Medium
- Customer Experience - Medium
- Operations - Low
Completeness of Vision — Relative Weighting
- Market Understanding - High
- Marketing Strategy - Medium
- Sales Strategy - Medium
- Offering (Product) Strategy - High
- Business Model - Medium
- Vertical/Industry Strategy - Low
- Innovation - High
- Geographic Strategy - Low
FAQs
Q: What does this research cover?
A: This research covers the data science and machine learning platform market, evaluating vendors' ability to provide integrated software solutions for building, customizing, and deploying AI models using various techniques including classical machine learning, deep learning, and generative AI. The report assesses platforms that support code-based and low-code development, collaboration across data scientists and business users, automation and AI assistance throughout the data science lifecycle, and MLOps practices for deploying and monitoring models in production.
Q: Who should use this research?
A: This research should be used by data science leaders, machine learning leaders, AI leaders, analytics leaders, and IT decision-makers who are evaluating and selecting DSML platforms for their organizations. It is particularly relevant for those looking to understand vendor capabilities in supporting AI agents, autonomous workflows, GenAI applications, composite AI systems, and enterprise-scale AI deployment across multiple environments. The research helps buyers assess vendors based on their ability to execute and completeness of vision in the DSML platform market.
Q: What are the mandatory features of vendors included in this market?
A: Mandatory features for vendors included in this market are: (1) ability to import or connect to tabular data from various data management systems on-premises and in the cloud; (2) data preparation using transformation tools and packages; (3) code-based development environment; (4) building and evaluating models using a library of core data science and ML techniques; (5) collaboration and project management tools for multiple users and teams; (6) deployment, hosting and serving models for use in services and applications; (7) model life cycle management to promote, demote, retrain and retire models; and (8) administration and configuration management for user roles, permissions and resource allocation.
Q: What are some reasons for not being included in this report?
A:
- Withdrawal of DSML product offering and strategy change (e.g., Anaconda)
- Platform not recommended for predominantly code-centric data science teams (e.g., KNIME)
- Did not meet Customer Interest Index threshold for inclusion (e.g., Posit)
- Did not meet revenue or growth thresholds
- Insufficient geographic presence or customer base
- Go-to-market strategy not focused on professional data scientist persona
- Did not meet inclusion criteria for comprehensive DSML platform capabilities (e.g., Teradata, Aible)
Q: What differentiates Ability to Execute vs. Completeness of Vision?
A: Ability to Execute evaluates a vendor's current operational capabilities and track record - focusing on product quality, financial viability, sales execution, market responsiveness, marketing effectiveness, customer experience, and operations. It assesses the vendor's capability to address current market trends and maintain visibility and customer demand. Completeness of Vision evaluates a vendor's strategic understanding and future direction - focusing on market understanding, strategic positioning with GenAI/DSML, go-to-market approach, product strategy depth, business model evolution, vertical focus, innovation commitment, and geographic strategy. It assesses the vendor's ability to understand market trends across diverse user groups and recognize how emerging technologies can advance data-driven decision making and AI system development.
Reference
- Gartner, Magic Quadrant for Data Science and Machine Learning Platforms, 28 May 2025, ID G00822217
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