IBM named a Leader in the 2025 Gartner® Magic Quadrant™ for Data Science and Machine Learning Platforms

Data scientists and machine learning engineers face challenges with DSML projects across their enterprise. These challenges include ensuring responsible AI implementations, having an optimized AI stack to manage both costs and scale, and bringing automations to every corner of the enterprise. IBM’s watsonx portfolio of products and other innovations are designed to equip data scientists and machine learning engineers with the tools they need to build, deploy and govern applications across the enterprise.

We are proud to announce that IBM has been named a Leader in the 2025 Gartner Magic Quadrant for Data Science and Machine Learning Platforms (DSML).  This recognition emphasizes our commitment to delivering innovative solutions that empower data scientists and machine learning engineers to build and deploy impactful solutions across their business. IBM's suite of products combine to provide a flexible, AI focused and innovative approach to data science and machine learning. In an era where AI must move from experimentation to execution, IBM leads the way.

Teams need a range of tools and frameworks to build impactful products for their businesses. IBM's portfolio of products gives data scientists the flexibility and choice they need when building AI technology.

IBM’s watsonx.ai is an AI studio, designed for enterprises to build and deploy AI solutions with a unified collection of open-source and proprietary frameworks, models, tooling and deployment options. Alongside seamless integrations with their existing tools, languages and frameworks, teams can leverage IBM solutions wherever their data lives, without vendor lock-in. This level of choice empowers data scientists to choose the technologies that best suit their enterprise needs.

Data is the most valuable asset when building AI applications. But often, its complexity prevents data scientists and AI engineers from unlocking its full potential.  

IBM has radically simplified the data-for-AI stack, granting data scientists and AI engineers powerful, streamlined tools to unify, govern and activate both structured and unstructured data. Watsonx.data is an open, hybrid data lakehouse for the generative AI era with built in data lineage and transparency, and IBM also offers comprehensive data integration and data intelligence capabilities. IBM’s acquisition of DataStax will further IBM's leadership, enhancing vector search features while also strengthening IBM’s investment in open-source technology.

IBM's relentless innovation equips data scientists and AI engineers with pioneering tools.

IBM Granite AI models are open source, small and high-performing, offering exceptional inference efficiency and complete control over customization and deployment options. Meanwhile, BeeAI provides an open-source, no-code platform to discover, build and run agents, and watsonx.ai’s AutoAI for RAG can automate RAG pipeline development and deployment. IBM’s investment in these key areas of foundation models, automation and RAG are empowering continued innovation by giving data scientists the tools they need to take their projects from the playground to production.

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