Gartner has acknowledged Microsoft as a Chief within the 2022 Gartner® Magic Quadrant™ for Cloud AI Developer Companies, with Microsoft positioned furthest in “Completeness of Imaginative and prescient”.
Gartner defines the market as “cloud-hosted or containerized providers that allow growth groups and enterprise customers who will not be information science specialists to make use of AI fashions through APIs, software program growth kits (SDKs), or functions.”
We’re proud to be acknowledged for our Azure AI Platform. On this submit, 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 be taught 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. For that reason, 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 information 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 firms use Azure AI in the present day, spanning industries and use instances.
An increasing number of, 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. In the present day, we’re pushing the boundaries of language mannequin capabilities with massive fashions like Turing, GPT-3, and Codex (the mannequin powering) to assist builders be extra productive. Azure AI packages these improvements into production-ready common fashions referred to as and use case-specific fashions, for builders to combine through API or an SDK, then proceed to tremendous tune for higher accuracy.
For builders and information 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 growth. This frees up information 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 information scientists will turn out to be more and more vital.”—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, Microsoft is “constructing fashions as platforms in Azure” in order that builders with totally different abilities can benefit from 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 .
Azure AI empowers builders to construct apps of their most well-liked language and deploy within the cloud, on-premises, or on the edge utilizing containers. Lately we additionally introduced the potential toand lengthen machine studying to run near the place your information lives. These sources might be run by a single pane with the administration, consistency, and reliability offered by .
Operationalize Accountable AI practices
“Distributors and prospects alike are looking for 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 ourto our product technique and growth lifecycle, and we’ve made it a precedence to assist prospects . We additionally present instruments and sources to assist prospects perceive, shield, and management their AI options, together with a , , and built-in instruments to assist them clarify mannequin habits, take a look at for equity, and extra. Offering a constant toolset to your information science workforce not solely helps accountable AI implementation but in addition helps present higher transparency and allows extra constant, environment friendly mannequin deployments.
Microsoft is proud to be acknowledged as a Chief in Cloud AI Developer Companies, and we’re excited by improvements occurring at Microsoft and throughout the trade that empower builders to sort out real-world challenges with AI. You’ll be able to learn and be taught from the.
Gartner Inc.: “,” Van Baker, Svetlana Sicular, Erick Brethenoux, Arun Batchu, Mike Fang, Might 23, 2022.
Gartner and Magic Quadrant are registered logos 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 printed by Gartner, Inc. as half of a bigger analysis doc and needs to be evaluated within the context of all the doc. The Gartner doc is accessible 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 solely these distributors with the best rankings or different designation. Gartner analysis publications encompass the opinions of Gartner’s analysis group and shouldn’t be construed as statements of reality. Gartner disclaims all warranties, expressed or implied, with respect to this analysis, together with any warranties of merchantability or health for a selected function.