We’re proud to share that Microsoft has as soon as once more been named a Chief within the 2025 Gartner® Magic Quadrant™ for Information Science and Machine Studying (DSML) Platforms.
We’re proud to share that Microsoft has as soon as once more been named a Chief within the 2025 Gartner® Magic Quadrant™ for Information Science and Machine Studying (DSML) Platforms. We consider this recognition displays our continued dedication to offering organizations with a complete toolchain for constructing and deploying machine studying fashions and AI purposes, reworking how companies function. Azure Machine Studying is a part of a broad, interoperable ecosystem throughout Microsoft Cloth, Microsoft Purview, and inside Azure AI Foundry.
Gartner defines an information science and machine studying platform as an built-in set of code-based libraries and low-code tooling. These platforms help the impartial use and collaboration amongst knowledge scientists and their enterprise and IT counterparts, with automation and AI help by all levels of the information science life cycle, together with enterprise understanding, knowledge entry and preparation, mannequin creation, and sharing of insights. Additionally they help engineering workflows, together with the creation of knowledge, function, deployment, and testing pipelines. The platforms are supplied by way of desktop consumer or browser with supporting compute situations or as a completely managed cloud providing.
Main the best way in 2025
With Microsoft, we’re turning our media experience right into a aggressive benefit—and harnessing knowledge to construct manufacturers and drive enterprise development.
—Callum Anderson, International Director for DevOps and SRE at Dentsu.
At Microsoft, we envision a unified expertise the place knowledge scientists, AI engineers, builders, IT operations professionals, and enterprise customers come collectively to create purposes and handle the complete AI lifecycle throughout personas and tasks. To that finish, in November 2024, we introduced the supply of Azure AI Foundry—a platform that enables builders to design, customise, and handle AI purposes. Azure Machine Studying is a trusted workbench that exists on high of Azure AI Foundry and powers the underlying device chain expertise, with capabilities for mannequin customization, together with fine-tuning and RAG.
Advancing AI with Azure Machine Studying and clever brokers
As a part of Azure AI Foundry, the Foundry Agent Service empowers developer groups to orchestrate AI brokers that automate advanced, cross-functional workflows. Whether or not constructing options for software program engineering, enterprise course of automation, buyer help, or knowledge evaluation, Foundry Agent Service offers a sturdy, safe, and interoperable basis to operationalize AI brokers in manufacturing environments.
- With help for multi-agent orchestration, builders can design agent techniques that coordinate throughout duties, share state, recuperate from failures, and evolve flexibly as necessities change. These brokers could be grounded in enterprise data utilizing Microsoft Cloth, Bing, and SharePoint, whereas interacting with each proprietary and third-party instruments because of open requirements like MCP (Mannequin Context Protocol) and A2A (Agent2Agent).
- Builders can begin constructing domestically utilizing open-source frameworks like Semantic Kernel and AutoGen, and we’re on a transparent path towards delivering a unified SDK throughout the 2 frameworks and Azure AI Foundry that means that you can transfer from native experimentation to manufacturing in cloud with out rewriting any code. This ensures constant developer expertise—from preliminary prototyping to managed orchestration with observability and enterprise-grade management.
Collectively, Azure Machine Studying and Foundry Agent Service allow a future the place AI techniques are designed for enterprise use with scalability and safety in thoughts.
Leveraging AI fashions with Azure AI Foundry
Azure AI Foundry presents builders an revolutionary methodology of deploying and managing its over 11,000 AI fashions with instruments just like the Mannequin Router, Mannequin Leaderboard, and Mannequin Benchmarks.
- The Mannequin Leaderboard simplifies the comparability of mannequin efficiency throughout real-world duties, offering clear benchmark scores, task-specific rankings, and reside updates, enabling customers to pick out the excessive accuracy, quick throughput, or aggressive price-performance ratio effectively.
- Mannequin Benchmarks in Azure AI Foundry supply a streamlined strategy to examine mannequin efficiency utilizing standardized datasets, whereas additionally permitting prospects to guage fashions on their very own knowledge to determine one of the best match for his or her particular situations.
- Complementing this, the Mannequin Router—obtainable now for Azure OpenAI fashions—dynamically routes queries to probably the most appropriate giant language mannequin (LLM) by assessing elements akin to question complexity, price, and efficiency, making certain high-quality outcomes whereas minimizing compute bills.
These capabilities empower companies to deploy versatile and adaptive AI techniques with enterprise-grade efficiency, safety, and governance. With built-in innovation from Microsoft and its ecosystem, customers achieve entry to future-ready options that improve effectivity and scalability, making certain they keep forward within the quickly evolving AI panorama.
Optimizing AI efficiency with fine-tuning in Azure AI Foundry
High quality-tuning is a necessary device for organizations aiming to customise pre-trained AI fashions for particular duties, enhancing their efficiency, accuracy, and adaptableness, all whereas decreasing operational prices. High quality-tuning in Azure AI Foundry is powered by the underlying Azure Machine Studying device chain.
- With improvements akin to Reinforcement High quality-Tuning (RFT) utilizing the o4-mini mannequin, Azure AI Foundry permits builders to enhance reasoning, context-aware responses, and dynamic decision-making by reinforcement indicators. This adaptability is especially suited to purposes requiring ongoing studying, making it a great methodology for evolving enterprise logic and making certain fashions keep related in dynamic environments.
- Azure AI Foundry additional simplifies fine-tuning with options akin to International Coaching and the Developer Tier. International Coaching lowers prices by permitting mannequin customization throughout a number of Azure areas, giving builders flexibility and scalability whereas adhering to strict privateness insurance policies. The Developer Tier presents an inexpensive strategy to consider fine-tuned fashions, enabling simultaneous testing throughout deployments and empowering customers to decide on one of the best candidate for manufacturing with precision and effectivity.
Collectively, these capabilities allow builders and enterprises to unlock the complete potential of their AI techniques, driving innovation and effectivity within the quickly evolving digital panorama.
Enabling organizations to deploy AI options
From healthcare and finance to manufacturing and retail, prospects are utilizing Azure Machine Studying to unravel advanced issues, optimize operations, and unlock new enterprise fashions. Whether or not it’s deploying basis fashions, orchestrating AI brokers, or scaling real-time inference, Microsoft helps organizations flip knowledge into influence.
Start your journey with Azure Machine Studying
The migration to Azure is just the start. We’ve laid the muse to discover alternatives we may solely think about earlier than.
—Steve Fortune, Chief Digital and Expertise Officer at CSX.
Machine studying is revolutionizing the operational and aggressive panorama for companies within the digital age. It presents alternatives to optimize enterprise processes, enhance buyer experiences, and drive innovation. Azure Machine Studying serves as a sturdy and versatile platform for machine studying and knowledge science, enabling organizations to implement AI options responsibly and successfully.
Gartner, Magic Quadrant for Information Science and Machine Studying Platforms, By Afraz Jaffri, Maryam Hassanlou, Tong Zhang, Deepak Seth, Yogesh Bhatt, 28 Might 2025.
GARTNER is a registered trademark and repair mark of Gartner, Inc. and/or its associates within the U.S. and internationally, Magic Quadrant is a registered trademark of Gartner, Inc. and/or its associates and is used herein with permission. All rights reserved.
This graphic was revealed by Gartner, Inc. as half of a bigger analysis doc and needs to be evaluated within the context of the complete doc. The Gartner doc is out there upon request from [https://www.gartner.com/en/documents/6533902].
Gartner doesn’t endorse any vendor, services or products depicted in its analysis publications, and doesn’t advise expertise customers to pick out solely these distributors with the very best scores or different designation. Gartner analysis publications include the opinions of Gartner’s analysis group and shouldn’t be construed as statements of truth. Gartner disclaims all warranties, expressed or implied, with respect to this analysis, together with any warranties of merchantability or health for a selected objective.
