Effective Testing for Machine Learning (Part III)

Testing for ML Series

A progressive, step-by-step framework for developing robust ML projects

In the first two parts (part I and part II), we described a framework to increase our project robustness by incrementally adding more comprehensive testing. From smoke tests to integration tests, we transitioned from a basic project to one that ensures the generation of high-quality models.

This last part wraps up the series and has three objectives:

1. Define a strategy to decide …

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