ML Pipeline Testing
ML pipeline testing validates the end-to-end system around a model — data ingestion, feature engineering, preprocessing, the model call itself, and post-processing — checking that each stage produces correct output and that failures anywhere in the chain are caught rather than silently propagating a corrupted input into the model.
Most production AI failures aren't actually model failures — they're pipeline failures upstream of the model, where a broken feature-engineering step, a schema change in a data source, or a silent null value quietly changes what the model actually receives. Testing only the model in isolation misses this whole category of bug entirely, since the model can perform perfectly against clean test inputs and still produce garbage in production because what it's actually being fed no longer matches what it was tested against.
Effective pipeline testing checks each stage independently — does preprocessing produce the expected shape and range of output for known inputs — and also end-to-end, confirming a known raw input produces the expected final output all the way through, with particular attention to schema validation at every boundary. Catching a malformed or missing field at ingestion is far cheaper than debugging why the model's output degraded three stages downstream. Data pipeline changes deserve the same regression-testing discipline as code changes, since they're just as capable of silently breaking production.
