Domino Data Science Blog

We've blogged before about the importance of model validation, a process that ensures that the model is performing the way it was intended and that it solves the problem it was designed to solve. Validations and tests are key elements to building machine learning pipelines you can trust. We've also talked about incorporating tests in your pipeline, which many data scientists find problematic. The issues stem from the fact that not all data scientists feel confident about traditional code testing methods, but more importantly, data science is so much more than just code. When validating pipelines we need to think about verifying the data integrity, inspecting its distributions, validating data splits, model evaluation, model comparison etc. But how can we deal with such complexity and maintain consistency in our pipelines? Enter Deepchecks - an open-source Python package for testing and validating machine learning models and data.