Writing Robust Tests for Data & Machine Learning Pipelines
Eugene Yan
An article discusses testing strategies for data and machine learning pipelines, covering unit tests at row, column, and table levels, schema validation, and integration tests using Pandas and PyTest. The author demonstrates testing a recommendation system pipeline that transforms behavioral logs into item-level events, trains a CTR model, and performs batch inference, showing how different test scopes map to concrete implementations. The article suggests that smaller testing scopes provide shorter feedback loops and proposes methods to make pipeline tests less brittle when new data and logic are added.
Why it matters
Or why I should write fewer integration tests.
