Testing Machine Learning Pipelines

Testing Machine Learning Pipelines

Testing Machine Learning Pipelines. Putting an effort to test machine learning pipelines end to end will end a lot of wasted efforts. You need to develop end-to-end tests that can be executed as part of the continuous integration pipelines. If you have Machine Learning (ML) pipelines in production, you have to worry about backward compatibility of changes made to the pipeline.

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Synopsis

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. Once you have the sampled data, you can run the stable version, e.g., master, etc., of your ML pipeline to produce the expected result. When you have a feature branch, run the branch on sampled data, compare the actual result with the expected result, and consider it green when the difference is acceptable.

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testing machine-learning

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