DevOps for Cognitive Services

It is common to talk about “last mile” adoption problems for both Agile (usually traditional QA teams, in other cases, traditional business teams) and now DevOps (again QA and Data teams as the typical bottlenecks).

I have recently been working with the Microsoft Cognitive Toolkit (CTK), and asked myself the same question on how to create a continuous delivery pipeline for AI applications. If AI implementations grow as much as Gartner is predicting, the deploying AI apps have a good change of become the next “last mile” problem.

Researching on this, came across an article on DevOps for Data Science and AI: Team Data Science Process for Developer Operations, with the description “This article explores the Developer Operations (DevOps) functions that are specific to an Advanced Analytics and Cognitive Services solution implementation.”

However, instead of the (more recently) clear-cut how-to documentation that Microsoft has been providing as part of its VSTS products, this article seemed more like a list of building blocks that need to be distilled into at least a nice simple sample implementation.

Investigating the Azure Data Science Virtual Machine seems the next step to better understand how everything is connected. Among other things it has the CTK installed, which is what is attracting my attention in this area. As a production solution though, it is not the final answer, so to define a process to deliver from this machine as a development environment to a production business server, a lot has still to be done.

I will continue experimenting and will let you guys know next. My ultimate goal would be to use some version of Prolog.

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