E2E testing in machine learning involves testing the coupled parts of the pipeline to ensure they work together as expected, including the data pipeline, feature engineering, model training, and model serialization and export. The primary goal is to ensure that modules interact correctly when combined and that system and model standards are met.
E2E testing reduces the risk of deployment failure and ensures effective production operations. It is important not to have weak assertion sections. The goal of integration testing is to ensure that the pipeline makes sense, not that it is correct.
Integration testing ensures consistency by validating different parts of the machine learning workflow, finding system-wide issues such as data format mismatches or compatibility issues, and validating end-to-end functionality to ensure the system meets overall requirements from data collection to model output.
Machine learning systems are complex and fragile, so you need to add integration tests as soon as possible.
The following snippet is an integration test for the entire ML pipeline.
Integration tests require careful planning, given their complexity, resource requirements, and execution time. Even integration tests are best kept small. These tests can be complex to set up and maintain, especially as your system grows and evolves.