Testing Machine Learning Pipelines

If you do not have the time to read the full article, consider reading the 30 seconds version. If you have Machine Learning (ML) pipelines in production, you have to worry about backward compatibility of changes made to the pipeline. It may be tempting to increase test coverage, but a high test coverage cannot guarantee that your recent changes have not broken the pipeline or generated low quality results. To do that, you need to develop end-to-end tests that can be executed as part of the continuous integration pipelines. Developing such a test requires sampling the dataset that powers the pipeline from a run that produces acceptable result and on which you have an in-depth knowledge.