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 companies that allow growth groups and enterprise customers who usually are not information science consultants to make use of AI fashions by way of 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 complete 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. For that reason, cloud AI developer companies (CAIDS) are important instruments for software program engineering groups.”—Gartner
A staggering 87 % of AI tasks 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 corporations use Azure AI at the moment, spanning industries and use circumstances.
An increasing number of, we see builders speed up time to worth by utilizing pre-built and customizable AI fashions as constructing blocks for clever options. Microsoft Analysis has made important breakthroughs in AI through the years, being the primary to attain human parity throughout speech, imaginative and prescient, and language capabilities. At this time, we’re pushing the boundaries of language mannequin capabilities with massive 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 common fashions referred to as Azure Cognitive Providers and use case-specific fashions, Azure Utilized AI Providers for builders to combine by way of API or an SDK, then proceed to superb tune for larger accuracy.
For builders and information scientists trying to construct production-ready machine studying fashions at scale, we help automated machine studying also referred to as autoML. AutoML in Azure Machine Studying relies on breakthrough Microsoft analysis targeted on automating the time-consuming, iterative duties of machine studying mannequin growth. This frees up information scientists, analysts, and builders to concentrate on 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 grow to be more and more essential.”—Gartner
As AI turns into extra mainstream throughout organizations, it’s important that workers 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 completely different abilities can reap the benefits 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 by way of Microsoft Energy Platform.
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. Just lately we additionally introduced the aptitude to use any Kubernetes cluster and lengthen machine studying to run near the place your information lives. These sources could be run by way of a single pane with the administration, consistency, and reliability offered 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 choosing AutoML companies, 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 growth lifecycle, and we’ve made it a precedence to assist prospects do the identical. We additionally present instruments and sources to assist prospects perceive, defend, and management their AI options, together with a Accountable AI Dashboard, bot growth tips, 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 larger transparency and allows 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 trade that empower builders to sort out real-world challenges with AI. You possibly can learn and study from the full Gartner Magic Quadrant now.
¹Why do 87 % of information science tasks 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 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 revealed by Gartner, Inc. as half of a bigger analysis doc and ought to be evaluated within the context of the complete doc. The Gartner doc is out there upon request from Microsoft. Gartner doesn’t endorse any vendor, services or products depicted in its analysis publications, and doesn’t advise expertise 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 specific goal.