Testing and debugging machine learning (ML) systems differs significantly from testing and debugging traditional software. We cover the main steps fro

Machine Learning Models Debugging & Testing (1/2)

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2021-06-07 03:00:06

Testing and debugging machine learning (ML) systems differs significantly from testing and debugging traditional software. We cover the main steps from debugging your machine learning model all the way to monitoring your pipelines and testing in production. This will maximize your grip on your models dealing with real-life scenarios that can become critical.

In this article, we are more interested in tracking the machine learning models after they were initially created and evaluated. Thinking about model debugging naturally resonates with software debugging where you want to make sure that the ML model works as expected.

Once the model is working, the continuous process of optimization takes place to enhance the model’s quality for production-readiness. However, reaching production is not the end line milestone. It is only the beginning of the more challenging and important phase of the model’s success which is monitoring and testing in production.

In order to easily debug your ML model, you should follow the model development best practices. The most important ones can be summarized as follows:

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