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 imagine 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 information scientists and their enterprise and IT counterparts, with automation and AI help by means of all levels of the information science life cycle, together with enterprise understanding, information entry and preparation, mannequin creation, and sharing of insights. Additionally they help engineering workflows, together with the creation of information, function, deployment, and testing pipelines. The platforms are supplied through desktop consumer or browser with supporting compute cases or as a completely managed cloud providing.
Main the way in which in 2025
With Microsoft, we’re turning our media experience right into a aggressive benefit—and harnessing information to construct manufacturers and drive enterprise development.
—Callum Anderson, World Director for DevOps and SRE at Dentsu.
At Microsoft, we envision a unified expertise the place information scientists, AI engineers, builders, IT operations professionals, and enterprise customers come collectively to create purposes and handle your 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 prime of Azure AI Foundry and powers the underlying software chain know-how, 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 information evaluation, Foundry Agent Service offers a strong, safe, and interoperable basis to operationalize AI brokers in manufacturing environments.
- With help for multi-agent orchestration, builders can design agent programs that coordinate throughout duties, share state, get well from failures, and evolve flexibly as necessities change. These brokers may be grounded in enterprise data utilizing Microsoft Cloth, Bing, and SharePoint, whereas interacting with each proprietary and third-party instruments due to 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 lets you 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 programs are designed for enterprise use with scalability and safety in thoughts.
Leveraging AI fashions with Azure AI Foundry
Azure AI Foundry presents builders an modern technique 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 stay updates, enabling customers to pick the excessive accuracy, quick throughput, or aggressive price-performance ratio effectively.
- Mannequin Benchmarks in Azure AI Foundry provide a streamlined technique to evaluate mannequin efficiency utilizing standardized datasets, whereas additionally permitting prospects to judge fashions on their very own information to establish the perfect match for his or her particular eventualities.
- Complementing this, the Mannequin Router—obtainable now for Azure OpenAI fashions—dynamically routes queries to essentially the most appropriate massive language mannequin (LLM) by assessing elements similar 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 programs with enterprise-grade efficiency, safety, and governance. With built-in innovation from Microsoft and its ecosystem, customers acquire 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 an important software for organizations aiming to customise pre-trained AI fashions for particular duties, enhancing their efficiency, accuracy, and adaptableness, all whereas lowering operational prices. High quality-tuning in Azure AI Foundry is powered by the underlying Azure Machine Studying software chain.
- With improvements similar 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 means of reinforcement indicators. This adaptability is especially fitted to purposes requiring ongoing studying, making it an excellent technique for evolving enterprise logic and making certain fashions keep related in dynamic environments.
- Azure AI Foundry additional simplifies fine-tuning with options similar to World Coaching and the Developer Tier. World 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 technique to consider fine-tuned fashions, enabling simultaneous testing throughout deployments and empowering customers to decide on the perfect candidate for manufacturing with precision and effectivity.
Collectively, these capabilities allow builders and enterprises to unlock the complete potential of their AI programs, 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 information into impression.
Start your journey with Azure Machine Studying
The migration to Azure is just the start. We’ve laid the inspiration to discover alternatives we may solely think about earlier than.
—Steve Fortune, Chief Digital and Know-how 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 strong and versatile platform for machine studying and information 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.
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