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 aren’t 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 p.c 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 p.c of Fortune 100 firms use Azure AI as we speak, spanning industries and use circumstances.
Increasingly more, 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 basic fashions often known as Azure Cognitive Companies and use case-specific fashions, Azure Utilized AI Companies for builders to combine through API or an SDK, then proceed to effective tune for better accuracy.
For builders and information scientists seeking to construct production-ready machine studying fashions at scale, we assist 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 outdoors 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 necessary.”—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 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. Lately we additionally introduced the potential to use any Kubernetes cluster and lengthen machine studying to run near the place your information lives. These assets could be run by means of a single pane with the administration, consistency, and reliability supplied by Azure Arc.
Operationalize Accountable AI practices
“Distributors and clients alike are in search of extra than simply efficiency and accuracy from machine studying mannequin. When choosing 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 growth lifecycle, and we’ve made it a precedence to assist clients do the identical. We additionally present instruments and assets to assist clients perceive, shield, and management their AI options, together with a Accountable AI Dashboard, bot growth pointers, 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 staff not solely helps accountable AI implementation but in addition helps present better 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 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 be taught from the full Gartner Magic Quadrant now.
Gartner Inc.: “Magic Quadrant for Cloud AI Developer Companies,” Van Baker, Svetlana Sicular, Erick Brethenoux, Arun Batchu, Mike Fang, Might 23, 2022.
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