Gartner recognizes Microsoft as a Leader in the 2022 Gartner® Magic Quadrant™ for Cloud AI Developer Services, with Microsoft positioned highest for “Completeness of Vision.”
Gartner describes the market as “a cloud-hosted or containerized platform that enables development teams and business users who are not data science experts to consume AI models via APIs, software development kits (SDKs), or applications. services that have been provided”.
We are proud that our Azure AI platform has been recognized. In this post, we delve into Gartner’s assessment and what it means for developers, and provide access to the full reprint of the Gartner Magic Quadrant to learn more.
Scale intelligent apps with production-ready AI
“ModelOps practices are maturing, but most software engineering teams want AI capabilities that do not require advanced machine learning skills. is an essential tool for—Gartner
A staggering 87% of AI projects never reach production¹. Beyond the complexities of data preprocessing and building AI models, organizations address scalability, security, governance, and more to get models ready for production. That’s why over 85% of Fortune 100 companies are currently using Azure AI across industries and use cases.
Increasingly, developers are using pre-built, customizable AI models as building blocks for intelligent solutions to accelerate time to value. Microsoft Research has been making major breakthroughs in AI for years and was the first company to achieve human parity across speech, vision, and language capabilities. We are now pushing the boundaries of language model capabilities with large-scale models such as Turing, GPT-3, and Codex (model-enhancing models). GitHub Copilot) helps improve developer productivity. Azure AI packages these innovations into a general, operationally ready model called Azure Cognitive Services, and Azure Applied AI Services, which are use-case-specific models for developers to integrate via APIs or SDKs. , continue fine-tuning to improve accuracy.
It supports automated machine learning, also known as autoML, for developers and data scientists looking to build production-ready machine learning models at scale. His AutoML in Azure Machine Learning builds on groundbreaking Microsoft research focused on automating the time-consuming and repetitive tasks of machine learning model development. This allows data scientists, analysts, and developers to focus on non-operational, value-added tasks and reduce time to production.
Make AI teams more productive across your organization
“As more developers use CAIDS to build machine learning models, collaboration between developers and data scientists will become increasingly important.”—Gartner
As AI becomes more mainstream across organizations, it’s imperative that employees have the tools they need to collaborate, build, manage, and deploy AI solutions effectively and responsibly.as Satya Nadella, Chairman and CEO of Microsoft Share on Microsoft BuildMicrosoft is “building a model as a platform on Azure” for developers of all skill sets to take breakthrough AI research and embed it into their own applications. This ranges from professional developers using APIs and SDKs to build intelligent apps, to citizen he developers using pre-built models. Microsoft Power Platform.
Azure AI enables developers to build apps in the language of their choice and deploy them to the cloud, on-premises, or at the edge using containers. We also recently announced the following features: Use any Kubernetes cluster Scale machine learning to run closer to where your data lives. These resources can be run in a single pane with the management, consistency, and reliability provided by Azure Arc.
Operationalize responsible AI practices
“Both vendors and customers want more than performance and accuracy from their machine learning models. We need to prioritize good vendors.”—Gartner
At Microsoft, Responsible AI standards Contributing to our product strategy and development lifecycle and helping our customers is a top priority. do the sameWe also provide tools and resources to help our customers understand, secure, and control their AI solutions. Responsible AI Dashboard, Bot development guidelines, and built-in tools to explain model behavior, test fairness, and more. Providing data science teams with a consistent set of tools not only supports responsible AI implementation, but also helps increase transparency and enable more consistent and efficient model deployment. increase.
Microsoft is proud to be recognized as a leader in cloud AI developer services, enabling developers to use AI to tackle real-world challenges that are happening at Microsoft and across the industry. We are excited about innovation. can be read and learned. Complete Gartner Magic Quadrant Now.
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References
¹Why do 87% of data science projects never go into production? venture beat.
Gartner: “Magic Quadrant for Cloud AI Developer Services,” Van Baker, Svetlana Sicular, Erick Brethenoux, Arun Batchu, Mike Fang, May 23, 2022.
Gartner and Magic Quadrant are US and international registered trademarks and service marks of Gartner, Inc. and/or its affiliates and are used herein with permission. all rights reserved. This figure was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. Gartner documentation is available upon request from Microsoft. Gartner does not endorse any vendor, product or service depicted in its research publications. Nor does it advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, express or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
