Building an Streaming Machine Learning System
October 1st, 2020
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Companies often want to know how customers perceive their product while they use it and when they post their experiences online. In this course we will build a system using Python that monitors Twitter posts and extracts the sentiments associated with a product through time. By the end of the course you'll be able to use this tool to keep track of how people feel about live events, company products, the latest sport matches, and any other topic people post on Twitter about.
Machine Learning (ML), as any other software implementation, must be tested to ensure it behaves as expected. Even more so in the case of ML, since it produces dynamic outputs based on changing inputs which often had not been seen before by the system, it is important for ML tests to look out for model drift by performing distribution tests, for example. In this post we go through various practical techniques that should be used to test production ML systems and things to look out for.