E2E testing in machine learning involves testing the combined parts of a pipeline to ensure they work together as expected. This includes data pipelines, 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.
Conducting E2E tests reduces the risk of deployment failures and ensures effective production operation. It’s important to keep the assertion section not brittle, the goal of the integration test is to make sure the pipeline makes sense, not that it’s correct.
Integration testing ensures cohesion by verifying that different parts of the machine learning workflow. It detects system-wide issues, such as data format inconsistencies and compatibility problems, and verifies end-to-end functionality, confirming that the system meets overall requirements from data collection to model output.
Since machine learning systems are complex and brittle, You should add integration tests as early as possible.
The following snippet is integration tests of the entire ML pipeline:
Integration tests require careful planning due to their complexity and resource demands and execution time. Even for integration tests, smaller ones are better. These tests can be complex to set up and maintain, especially as systems scale and evolve.