Making sense of Microsoft's approach to AI

Microsoft has a master plan for trying to get more customers to jump into the AI waters. Here's an attempt to explain how containers, accelerators, and APIs all figure in.

As the past year has made clear, Microsoft's strategy is to add AI (and stir) to all of its products and services. But is there some kind of higher-level method to Microsoft's AI madness?

There is, but Microsoft's all-up strategy when it comes to AI isn't necessarily readily apparent or quick to explain. Microsoft has an increasing number of commercial AI products and services, in addition to the AI work going on its its research unit. I found a recent blog post from Steve Guggenheimer, Corporate Vice President of Developer Platform and Evangelism, provided a good starting point for pulling this all together.

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Here's Guggenheimer's diagram of where Microsoft believes its customers should start with its AI technologies in his post, "The AI Journey."

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Credit: Microsoft

As Guggenheimer explains, Microsoft's idea is to let customers jump in where they are. Those on the lower end of the AI experience chain might want to begin dabbling with AI with business intelligence and apps. Microsoft's announcement this week about its plan to add AI capabilities to Power BI (as explained here by my ZDNet colleague Andrew Brust) is the cornerstone of this part of Microsoft's strategy.

For customers with a little more AI experience and who are willing to do a bit more customization, Microsoft's Dynamics 365 software-as-a-service apps -- especially those which recently got their own AI boost -- provides another place for customers to get their AI feet wet, Guggenheimer suggests.

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The next two pieces of Microsoft's AI strategy are where there's been a lot of announcements, as of late.

Microsoft is working on a number of AI "Accelerators," solution templates and analytics templates to give users a way to build on top of some repeatable patterns and practices around AI. Microsoft announced earlier this week a new solution accelerator for virtual assistantsthat it's making available on GitHub that is aimed at allowing customers and partners to build their own enterprise-grade "conversational assistant" that can be personalized for their customer base. This accelerator uses the Azure Bot Service under the covers.

Microsoft's plan is to try to get developers to write new apps and services using the Azure Bot Service or by calling specific application programming interfaces (APIs) that it offers through its so-called cognitive services. Users have the option of building on top of Microsoft's own vision, text, speech, knowledge and other cognitive services and/or to customize these APIs.

Microsoft has identified some key, repeatable patterns for AI solutions around things like business agents/bots; person/object/activity detection; knowledge mining of documents and video; and autonomous vehicle, networks and other kinds of systems. And it is targeting these around a handful of key vertical markets, like healthcare, insurance, finance, retail/marketing and manufacturing.

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Speaking of cognitive services, Microsoft is taking an interesting tack in making some of its cognitive services available for use in containers. The idea is by containerizing these APIs, Microsoft can make them available in cases where users can't use them easily in the public cloud.

As of this week, Microsoft made five AI capabilities available in containers in public preview form: key phrase extraction, language detection, sentiment analysis, face and emoticon detection and OCR/text recognition. More containerized services are planned, including speech and language understanding.

Customers have the option of running these APIs in containers right alongside apps that are also containerized. Microsoft achieved this by moving the model portion of the service into a container, which customers can deploy in the cloud, on the edge of a network or on-premises. Officials said that users will have complete control of their data and where it goes via this approach, since they can choose whether to process data in the cloud or locally using the containerized approach. (For more about Microsoft's containerized cognitive services plan, check out this video.)

For customers ready and able to go the fully customized AI route, Microsoft is touting its data infrastructure and tools to allow them to build their own models and tools from scratch. Users who are committed to storing data in Microsoft's Common Data Model can use it in Azure Data Lake. Those who need high-level horsepower to run their AI tasks can try out Microsoft's Project Brainwave, which allows users to make use of Microsoft's own field-programmable gate array systems.

This outline doesn't include all of Microsoft's AI-imbued products and services, but it does provide a frame of reference as to where Microsoft is going to point developers in the future.

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