In the 2022 Gartner® Magic Quadrant™ Cloud Computing AI Developer Service, Microsoft's services have been recognized by Gartner and are at the farthest end of the "Vision Completeness" axis.
According to Gartner, these "cloud-hosted or containerized services enable non-data science developers and practitioners to consume AI models through APIs, software development kits (SDKs), or applications."
We are very excited to be recognized for Azure AI Platform. In this article, we'll take a deep dive into the Gartner assessment and what it means for developers, as well as provide resources for a full reprint of the Gartner Magic Quadrant so you can learn more about it.
Extend intelligent applications with production-ready AI models
“Although ModelOps practices are becoming more mature, most software engineering teams still need AI capabilities that do not rely on advanced machine learning. For this reason, Cloud AI Developer Services (CAIDS) is an essential tool for software engineering teams.” — — Gartner
As many as 87% of all AI projects never make it to production¹. In addition to completing complex data preprocessing and AI modeling, companies also struggle with scalability, security, governance, and other aspects to get models ready for production. That's why more than 85% of Fortune 100 companies today choose Azure AI for a variety of use cases across different industries.
Today, for faster time to value, more and more developers use pre-made and customizable AI models to build intelligent solutions. Over the years, Microsoft Research has made several major breakthroughs in AI, including the first to achieve human immersion in speech, vision and language abilities. We are currently enabling breakthrough language features through large models such as Turing, GPT-3, and Codex (a model that supports GitHub Copilot), thereby increasing developer productivity. Azure AI brings all of these innovations together into a production-ready family of generic models called Azure Cognitive Services and Use-Case-Specific Models. Using the Azure App AI service, developers can integrate models via API or SDK, and then continue to fine-tune for higher accuracy.
For developers and data scientists looking to build production-ready machine learning models at scale, we support the use of automated machine learning called AutoML. Part of Azure Machine Learning, AutoML grew out of Microsoft's breakthrough research focused on automating the time-consuming and iterative tasks of machine learning model development. Its role is to free data scientists, analysts and developers to focus on non-operational value-added tasks and to speed up time-to-production of projects.
Boost the productivity of your organization's AI team
"As more developers use CAIDS to build machine learning models, collaboration between developers and data scientists is becoming increasingly important." - Gartner
As AI continues to permeate the workplace, organizations must provide employees with the tools necessary to effectively and responsibly collaborate, build, manage, and deploy AI solutions. As Microsoft Chairman and CEO Satya Nadella shared at the Microsoft Build conference, Microsoft is "building models to the standards of the Azure platform," with the goal of making breakthrough AI research accessible to developers of all skills Integrate into your own application. This includes both professional developers building smart applications using APIs and SDKs, and amateur developers using Microsoft Power Platform pre-built models.
Using Azure AI, developers can build applications in their preferred language and deploy them in the cloud, on-premises or at the edge via containers. Recently, we also announced support for deploying operations close to where the data resides using any Kubernetes cluster and scaling machine learning. These resources can run in a single pane of glass with the management, consistency, and reliability provided by Azure Arc.
Implementing the Principles of Responsible AI
"Machine learning models are about more than just performance and accuracy for both suppliers and customers. When choosing AutoML services, they should pay more attention to explainable and transparent models with built-in bias detection and compensation mechanisms." — — Gartner
Microsoft's product strategy and development lifecycle follow the principles of Responsible AI. We also make it a priority for our customers to follow these guidelines as well. To that end, we provide tools and resources to help clients understand, secure and control their AI solutions. These tools and resources include the Responsible AI Dashboard, bot development guides, and more built-in tools for explaining model behavior, testing fairness, and more. In addition to supporting the implementation of Responsible AI guidelines, providing data science teams with such a consistent set of tools can help increase transparency and improve the efficiency and consistency of model deployment.
We're very proud of Microsoft being recognized as a "leader" in AI developer services in the cloud, and we're excited about the innovations at Microsoft and the AI industry as a whole that enable developers to use AI to solve real-world problems. challenge. For more details, view the full Gartner Magic Quadrant.
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References
¹Why 87% of data science projects fail to go into production? Venture Beat.
Gartner Inc.: “ Magic Quadrant for Cloud AI Developer Services ,” Van Baker, Svetlana Sicular, Erick Brethenoux, Arun Batchu, Mike Fang, 23 May 2022.
Gartner and the Magic Quadrant are registered trademarks and service marks of Gartner, Inc. and/or its affiliates in the United States and worldwide. Use of this article is with permission. all rights reserved. This graph is from a large research paper published by Gartner, Inc. and its utility should be assessed accordingly. Gartner provides documentation upon request from Microsoft. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other characteristics. Gartner research publications represent 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.
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