How to Properly Test ML Models & Data

Automatic testing for ML pipelines is hard. Part of the executed code is a model that was dynamically trained on a fresh batch of data, and silent failures are common. Therefore, it’s problematic to use known methodologies such as automating tests for predefined edge cases and tracking code coverage. In this talk we’ll discuss common pitfalls with ML models, and cover best practices for automatically validating them: What should be tested in these pipelines? How can we verify that they'll behave as we expect once in production? We’ll demonstrate how to automate tests for these scenarios and introduce open-source testing tools that can aid the process.

Who is this presentation for?
Data scientists, Machine Learning Engineers and Data and AI managers

Prerequisite knowledge:
Experience with training ML models