Gartner has acknowledged Microsoft as a Chief within the 2022 Gartner® Magic Quadrant™ for Cloud AI Developer Providers, with Microsoft positioned furthest in “Completeness of Imaginative and prescient”.
Gartner defines the market as “cloud-hosted or containerized providers that allow improvement groups and enterprise customers who are usually not knowledge science specialists to make use of AI fashions through APIs, software program improvement kits (SDKs), or functions.”
We’re proud to be acknowledged for our Azure AI Platform. On this put up, we’ll dig into the Gartner analysis, what it means for builders, and supply entry to the total reprint of the Gartner Magic Quadrant to study extra.
Scale clever apps with production-ready AI
“Though ModelOps practices are maturing, most software program engineering groups nonetheless want AI capabilities that don’t demand superior machine studying abilities. Because of this, cloud AI developer providers (CAIDS) are important instruments for software program engineering groups.”—Gartner
A staggering 87 % of AI initiatives by no means make it into manufacturing.¹ Past the complexity of knowledge preprocessing and constructing AI fashions, organizations wrestle with scalability, safety, governance, and extra to make their mannequin’s manufacturing prepared. That’s why over 85 % of Fortune 100 corporations use Azure AI at the moment, spanning industries and use instances.
Increasingly more, we see builders speed up time to worth through the use of pre-built and customizable AI fashions as constructing blocks for clever options. Microsoft Analysis has made vital breakthroughs in AI through the years, being the primary to attain human parity throughout speech, imaginative and prescient, and language capabilities. As we speak, we’re pushing the boundaries of language mannequin capabilities with giant fashions like Turing, GPT-3, and Codex (the mannequin powering GitHub Copilot) to assist builders be extra productive. Azure AI packages these improvements into production-ready normal fashions generally known as Azure Cognitive Providers and use case-specific fashions, Azure Utilized AI Providers for builders to combine through API or an SDK, then proceed to nice tune for better accuracy.
For builders and knowledge scientists seeking to construct production-ready machine studying fashions at scale, we help automated machine studying also called autoML. AutoML in Azure Machine Studying relies on breakthrough Microsoft analysis centered on automating the time-consuming, iterative duties of machine studying mannequin improvement. This frees up knowledge scientists, analysts, and builders to deal with value-add duties exterior operations and speed up their time to manufacturing.
Allow productiveness for AI groups throughout the group
“As extra builders use CAIDS to construct machine studying fashions, the collaboration between builders and knowledge scientists will develop into more and more necessary.”—Gartner
As AI turns into extra mainstream throughout organizations, it’s important that staff have the instruments they should collaborate, construct, handle, and deploy AI options successfully and responsibly. As Microsoft Chairman and CEO Satya Nadella shared at Microsoft Construct, Microsoft is “constructing fashions as platforms in Azure” in order that builders with totally different abilities can make the most of breakthrough AI analysis and embed them into their very own functions. This ranges from skilled builders constructing clever apps with APIs and SDKs to citizen builders utilizing pre-built fashions through Microsoft Energy Platform.
Azure AI empowers builders to construct apps of their most popular language and deploy within the cloud, on-premises, or on the edge utilizing containers. Just lately we additionally introduced the aptitude to use any Kubernetes cluster and prolong machine studying to run near the place your knowledge lives. These assets might be run by way of a single pane with the administration, consistency, and reliability supplied by Azure Arc.
Operationalize Accountable AI practices
“Distributors and prospects alike are in search of extra than simply efficiency and accuracy from machine studying mannequin. When deciding on AutoML providers, they need to prioritize distributors that excel at offering explainable, clear fashions with built-in bias detection and compensatory mechanisms.”—Gartner
At Microsoft, we apply our Accountable AI Normal to our product technique and improvement lifecycle, and we’ve made it a precedence to assist prospects do the identical. We additionally present instruments and assets to assist prospects perceive, defend, and management their AI options, together with a Accountable AI Dashboard, bot improvement pointers, and built-in instruments to assist them clarify mannequin conduct, check for equity, and extra. Offering a constant toolset to your knowledge science workforce not solely helps accountable AI implementation but in addition helps present better transparency and permits extra constant, environment friendly mannequin deployments.
Microsoft is proud to be acknowledged as a Chief in Cloud AI Developer Providers, and we’re excited by improvements taking place at Microsoft and throughout the business that empower builders to deal with real-world challenges with AI. You’ll be able to learn and study from the full Gartner Magic Quadrant now.
Be taught extra
¹Why do 87 % of knowledge science initiatives by no means make it into manufacturing? Enterprise Beat.
Gartner Inc.: “Magic Quadrant for Cloud AI Developer Providers,” Van Baker, Svetlana Sicular, Erick Brethenoux, Arun Batchu, Mike Fang, Might 23, 2022.
Gartner and Magic Quadrant are registered emblems and repair marks of Gartner, Inc. and/or its associates within the U.S. and internationally and are used herein with permission. All rights reserved. This graphic was revealed by Gartner, Inc. as half of a bigger analysis doc and ought to be evaluated within the context of the whole doc. The Gartner doc is obtainable upon request from Microsoft. 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 very best scores or different designation. Gartner analysis publications encompass the opinions of Gartner’s analysis 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 function.