Machine learning models: Solving issues in production and beyond

Session
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Given how much effort goes into creating a good machine learning model, one might assume that all of the hard work will be done once the model is finally ready for production. In actuality, taking the model to production is just the very first step. Once it is there, you also have to deal with issues that many would-be machine learning users don’t even consider when they start out. It doesn’t matter if you use sophisticated deep learning models, simple decision trees, or even the most basic linear regression model that Excel can produce, many of those issues will plague any system that builds models on data.

In this talk, I will introduce you to issues like incorporating user feedback, concept drift, and unhealthy feedback loops. You’ll learn how to recognize if these kinds of issues could affect you now or in the future, as well as  what you can do to solve these problems.

Behind the Tracks