Tying everything together – Solving a Machine Learning problem in the Cloud (Part 4of 4)

This is the final, part 4 of a four-part series that breaks up a talk that I gave at the Toronto AI Meetup. Part 1, Part 2 and Part 3 [https://channel9.msdn.com/Shows/AI-Sh...]  were all about the foundations of machine learning, optimization, models, and even machine learning in the cloud. In this video I show an actual machine learning problem (see the GitHub repo for the code https://aka.ms/GitHub/CloudScaleML) that does the important job of distinguishing between tacos and burritos (an important problem to be sure). The primary concepts included is MLOps both on the machine learning side as well as the deliver side in Azure Machine Learning and Azure DevOps respectively.

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