I enjoyed giving my “Intro to Amazon Machine Learning” talk at the AWS Denver Boulder meetup.   (Shout out to an old friend and colleague who came out to see it.) I didn’t get through the whole pipeline demonstration (I didn’t get a chance to do the batch prediction), but the demo gods were kind and the demo went well.

We also had a good discussion.  A few folks present had used machine learning before, so we talked about where AML made sense (hint, it’s not a fit for every problem).  Also had some good questions about AML, about performance and pricing.  One of the members shared a reinvent anecdote: the AML team looked at all the machine learning used in Amazon and graphed the use cases and solved for the most common ones.

As, usual, I also learned something. OpenRefine is a tool to help you prepare data for machine learning.  And when you change the score cut-off, you need to restart your real-time end point.

The “Intro to Amazon Machine Learning” slides are up on SlideShare, and big thanks to the Meetup organizers.

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