How to Test and Monitor Machine Learning Model Deployments

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For years, businesses and developers have understood the importance of testing software before deployment. Before it can interface with customers in real time, a business naturally wants the software to function as expected. With the increasing demand for machine learning implemented in business, it’s reasonable to expect that machine learning models deployed into production need to be tested just as rigorously.

However, for many organizations, machine learning model deployments are relatively new, and some don’t have sufficient knowledge or a foundation in place to test them as rigorously as they test software. Though extensive testing of these models needs to happen in research and development, many other problems can also occur once live data enters the model.

In this blog post, I’ll give a brief overview of what machine learning model deployment means and entails, along with some of the differences between testing these models in deployment as opposed to standard software. Next, I’ll discuss why testing machine learning models is important and the challenges these models might face after deployment. Finally, I’ll discuss methods for testing in order to address these challenges.

For details on the technical implementation of testing and monitoring machine learning model deployments, visit…