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, remodeling how companies function. Azure Machine Studying is a part of a broad, interoperable ecosystem throughout Microsoft Material, Microsoft Purview, and inside Azure AI Foundry.
Gartner defines a knowledge science and machine studying platform as an built-in set of code-based libraries and low-code tooling. These platforms assist the impartial use and collaboration amongst information scientists and their enterprise and IT counterparts, with automation and AI help by way of all phases of the info science life cycle, together with enterprise understanding, information entry and preparation, mannequin creation, and sharing of insights. In addition they assist engineering workflows, together with the creation of information, characteristic, deployment, and testing pipelines. The platforms are offered through desktop shopper or browser with supporting compute cases 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 information 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 information scientists, AI engineers, builders, IT operations professionals, and enterprise customers come collectively to create purposes and handle your entire 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 instrument 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 assist, or information evaluation, Foundry Agent Service offers a strong, safe, and interoperable basis to operationalize AI brokers in manufacturing environments.
- With assist for multi-agent orchestration, builders can design agent methods that coordinate throughout duties, share state, get better from failures, and evolve flexibly as necessities change. These brokers could be grounded in enterprise data utilizing Microsoft Material, 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 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 methods 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 stay 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 technique to evaluate mannequin efficiency utilizing standardized datasets, whereas additionally permitting prospects to guage fashions on their very own information to establish the very best match for his or her particular eventualities.
- Complementing this, the Mannequin Router—out there now for Azure OpenAI fashions—dynamically routes queries to probably the most appropriate massive language mannequin (LLM) by assessing components corresponding to question complexity, price, and efficiency, guaranteeing high-quality outcomes whereas minimizing compute bills.
These capabilities empower companies to deploy versatile and adaptive AI methods 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, guaranteeing they keep forward within the quickly evolving AI panorama.
Optimizing AI efficiency with fine-tuning in Azure AI Foundry
Positive-tuning is a necessary instrument for organizations aiming to customise pre-trained AI fashions for particular duties, enhancing their efficiency, accuracy, and adaptableness, all whereas lowering operational prices. Positive-tuning in Azure AI Foundry is powered by the underlying Azure Machine Studying instrument chain.
- With improvements corresponding to Reinforcement Positive-Tuning (RFT) utilizing the o4-mini mannequin, Azure AI Foundry permits builders to enhance reasoning, context-aware responses, and dynamic decision-making by way of reinforcement indicators. This adaptability is especially fitted to purposes requiring ongoing studying, making it a super methodology for evolving enterprise logic and guaranteeing fashions keep related in dynamic environments.
- Azure AI Foundry additional simplifies fine-tuning with options corresponding 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 reasonably priced technique to consider fine-tuned fashions, enabling simultaneous testing throughout deployments and empowering customers to decide on the very best candidate for manufacturing with precision and effectivity.
Collectively, these capabilities allow builders and enterprises to unlock the total potential of their AI methods, 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 muse 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 Could 2025.
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