Microsoft is a Chief within the 2024 Gartner® Magic Quadrant™ for Knowledge Science and Machine Studying Platforms 

Microsoft is a Chief within the 2024 Gartner® Magic Quadrant™ for Knowledge Science and Machine Studying Platforms 
Microsoft is a Chief within the 2024 Gartner® Magic Quadrant™ for Knowledge Science and Machine Studying Platforms 


Microsoft is a Chief on this 12 months’s Gartner® Magic Quadrant™ for Knowledge Science and Machine Studying Platforms. Azure AI supplies a robust, versatile end-to-end platform for accelerating knowledge science and machine studying innovation.

Microsoft is a Chief on this 12 months’s Gartner® Magic Quadrant™ for Knowledge Science and Machine Studying Platforms. Azure AI supplies a robust, versatile end-to-end platform for accelerating knowledge science and machine studying innovation whereas offering the enterprise governance that each group wants within the period of AI. 

a man and a woman looking at a computer screen

In Might 2024, Microsoft was additionally named a Chief for the fifth 12 months in a row within the Gartner® Magic Quadrant™ for Cloud AI Developer Services, the place we positioned furthest for our Completeness of Imaginative and prescient. We’re happy by these recognitions from Gartner as we proceed serving to prospects, from giant enterprises to agile startups, convey their AI and machine studying fashions and functions into manufacturing securely and at scale. 

Azure AI is on the forefront of purpose-built AI infrastructure, accountable AI tooling, and serving to cross-functional groups collaborate successfully utilizing Machine Studying Operations (MLOps) for generative AI and conventional machine studying tasks. Azure Machine Learning supplies entry to a broad choice of basis fashions within the Azure AI mannequin catalog—together with the current releases of Phi-3, JAIS, and GPT-4o—and instruments to fine-tune or construct your personal machine studying fashions. Moreover, the platform helps a wealthy library of open-source frameworks, instruments, and algorithms in order that knowledge science and machine studying groups can innovate in their very own manner, all on a trusted basis. 

Speed up time to worth with Azure AI infrastructure

We’re now in a position to get a functioning mannequin with related insights up and working in simply a few weeks because of Azure Machine Studying. We’ve even managed to provide verified fashions in simply 4 to 6 weeks.”

Dr. Nico Wintergerst, Employees AI Analysis Engineer at relayr GmbH

Azure Machine Studying helps organizations construct, deploy, and handle high-quality AI options rapidly and effectively, whether or not constructing giant fashions from scratch, working inference on pre-trained fashions, consuming fashions as a service, or fine-tuning fashions for particular domains. Azure Machine Studying runs on the identical powerful AI infrastructure that powers a few of the world’s hottest AI companies, reminiscent of ChatGPT, Bing, and Azure OpenAI Service. Moreover, Azure Machine Studying’s compatibility with ONNX Runtime and DeepSpeed might help prospects additional optimize coaching and inference time for efficiency, scalability, and energy effectivity.

Whether or not your group is coaching a deep studying mannequin from scratch utilizing open supply frameworks or bringing an current mannequin into the cloud, Azure Machine Studying permits knowledge science groups to scale out coaching jobs utilizing elastic cloud compute assets and seamlessly transition from coaching to deployment. With managed online endpoints, prospects can deploy fashions throughout highly effective CPU and graphics processing unit (GPU) machines without having to handle the underlying infrastructure—saving effort and time. Equally, prospects don’t must provision or handle infrastructure when deploying foundation models as a service from the Azure AI mannequin catalog. This implies prospects can simply deploy and handle hundreds of fashions throughout manufacturing environments—from on-premises to the sting—for batch and real-time predictions.  

Streamline operations with versatile MLOps and LLMOps 

Immediate circulation helped streamline our improvement and testing cycles, which established the groundedness we required for ensuring the client and the answer have been interacting in a practical manner.”

Fabon Dzogang, Senior Machine Studying Scientist at ASOS

Machine learning operations (MLOps) and large language model operations (LLMOps) sit on the intersection of individuals, processes, and platforms. As knowledge science tasks scale and functions change into extra complicated, efficient automation and collaboration instruments change into important for reaching high-quality, repeatable outcomes.  

Azure Machine Studying is a versatile MLOps platform, constructed to assist knowledge science groups of any measurement. The platform makes it simple for groups to share and govern machine studying belongings, construct repeatable pipelines utilizing built-in interoperability with Azure DevOps and GitHub Actions, and repeatedly monitor mannequin efficiency in manufacturing. Knowledge connectors with Microsoft sources reminiscent of Microsoft Fabric and external sources reminiscent of Snowflake and Amazon S3, additional simplify MLOps. Interoperability with MLflow additionally makes it seamless for knowledge scientists to scale current workloads from native execution to the cloud and edge, whereas storing all MLflow experiments, run metrics, parameters, and mannequin artifacts in a centralized workspace. 

Azure Machine Learning prompt flow helps streamline your entire improvement cycle for generative AI functions with its LLMOps capabilities, orchestrating executable flows comprised of fashions, prompts, APIs, Python code, and instruments for vector database lookup and content material filtering. Azure AI immediate circulation can be utilized along with well-liked open-source frameworks like LangChain and Semantic Kernel, enabling builders to convey experimental flows into immediate circulation to scale these experiments and run complete evaluations. Builders can debug, share, and iterate on functions collaboratively, integrating built-in testing, tracing, and analysis instruments into their CI/CD system to repeatedly reassess the standard and security of their software. Then, builders can deploy functions when prepared with one click on and monitor flows for key metrics reminiscent of latency, token utilization, and era high quality in manufacturing. The result’s end-to-end observability and steady enchancment. 

Develop extra reliable fashions and apps 

The accountable AI dashboard supplies invaluable insights into the efficiency and conduct of pc imaginative and prescient fashions, offering a greater degree of understanding into why some fashions carry out in another way than others, and insights into how numerous underlying algorithms or parameters affect efficiency. The profit is better-performing fashions, enabled and optimized with much less effort and time.” 

—Teague Maxfield, Senior Supervisor at Constellation Clearsight 

AI ideas reminiscent of equity, security, and transparency usually are not self-executing. That’s why Azure Machine Studying supplies knowledge scientists and builders with sensible instruments to operationalize accountable AI proper of their circulation of labor, whether or not they should assess and debug a conventional machine studying mannequin for bias, shield a basis mannequin from immediate injection assaults, or monitor mannequin accuracy, high quality, and security in manufacturing. 

The Responsible AI dashboard helps knowledge scientists assess and debug conventional machine studying fashions for equity, accuracy, and explainability all through the machine studying lifecycle. Customers can even generate a Responsible AI scorecard to doc and share mannequin efficiency particulars with enterprise stakeholders, for extra knowledgeable decision-making. Equally, builders in Azure Machine Studying can overview mannequin playing cards and benchmarks and carry out their very own evaluations to pick out the very best basis mannequin for his or her use case from the Azure AI model catalog. Then they will apply a defense-in-depth method to mitigating AI dangers utilizing built-in capabilities for content filtering, grounding on fresh data, and immediate engineering with safety system messages. Evaluation tools in immediate circulation allow builders to iteratively measure, enhance, and doc the affect of their mitigations at scale, utilizing built-in metrics and customized metrics. That manner, knowledge science groups can deploy options with confidence whereas offering transparency for enterprise stakeholders. 

Learn extra on Responsible AI with Azure.

Ship enterprise safety, privateness, and compliance 

We wanted to decide on a platform that supplied best-in-class safety and compliance because of the delicate knowledge we require and one which additionally provided best-in-class companies as we didn’t wish to be an infrastructure internet hosting firm. We selected Azure due to its scalability, safety, and the immense assist it provides when it comes to infrastructure administration.”

—Michael Calvin, Chief Technical Officer at Kinectify

In at present’s data-driven world, efficient knowledge safety, governance, and privateness require each group to have a complete understanding of their knowledge and AI and machine studying programs. AI governance additionally requires efficient collaboration between various stakeholders, reminiscent of IT directors, AI and machine studying engineers, knowledge scientists, and threat and compliance roles. Along with enabling enterprise observability via MLOps and LLMOps, Azure Machine Studying helps organizations be certain that knowledge and fashions are protected and compliant with the best requirements of safety and privateness.

With Azure Machine Studying, IT directors can limit entry to assets and operations by person account or teams, management incoming and outgoing community communications, encrypt knowledge each in transit and at relaxation, scan for vulnerabilities, and centrally handle and audit configuration insurance policies via Azure Policy. Knowledge governance groups can even join Azure Machine Studying to Microsoft Purview, in order that metadata on AI belongings—together with fashions, datasets, and jobs—is mechanically revealed to the Microsoft Purview Knowledge Map. This allows knowledge scientists and knowledge engineers to watch how elements are shared and reused and study the lineage and transformations of coaching knowledge to know the affect of any points in dependencies. Likewise, threat and compliance professionals can monitor what knowledge is used to coach fashions, how base fashions are fine-tuned or prolonged, and the place fashions are employed throughout totally different manufacturing functions, and use this as proof in compliance reviews and audits. 

Lastly, with the Azure Machine Learning Kubernetes extension enabled by Azure Arc, organizations can run machine studying workloads on any Kubernetes clusters, guaranteeing knowledge residency, safety, and privateness compliance throughout hybrid public clouds and on-premises environments. This permits organizations to course of knowledge the place it resides, assembly stringent regulatory necessities whereas sustaining flexibility and management over their MLOps. Clients utilizing federated learning techniques together with Azure Machine Studying and Azure confidential computing can even prepare highly effective fashions on disparate knowledge sources, all with out copying or shifting knowledge from safe areas. 

Get began with Azure Machine Studying 

Machine studying continues to rework the best way companies function and compete within the digital period—whether or not you wish to optimize your online business operations, improve buyer experiences, or innovate. Azure Machine Learning supplies a robust, versatile machine studying and knowledge science platform to operationalize AI innovation responsibly.  


*Gartner, Magic Quadrant for Knowledge Science and Machine Studying Platforms, By Afraz Jaffri, Aura Popa, Peter Krensky, Jim Hare, Raghvender Bhati, Maryam Hassanlou, Tong Zhang, 17 June 2024. 

Gartner, Magic Quadrant for Cloud AI Developer Providers, Jim Scheibmeir, Arun Batchu, Mike Fang, Revealed 29 April 2024. 

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. 

Gartner doesn’t endorse any vendor, services or products depicted in its analysis publications and doesn’t advise know-how customers to pick out solely these distributors with the best scores or different designation. Gartner analysis publications encompass the opinions of Gartner’s Analysis & Advisory 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. 

This graphic was revealed by Gartner, Inc. as half of a bigger analysis doc and must be evaluated within the context of your entire doc. The Gartner doc is accessible upon request from this link. 



Leave a Reply

Your email address will not be published. Required fields are marked *