Testing Machine Learning

August 26th, 2020

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.


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I help companies through digital transformations by designing and developing systems that improve human decision making with data, cloud, software, and mathematics (Machine Learning, Operations Research, Statistics). I also enjoy coaching teams and publishing content on Smart Automation.


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