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Data Quality: Testing and Validation in Pipelines
How to test data quality in pipelines: the dimensions worth checking, nine concrete checks written as SQL that counts failing rows and run on three million real taxi trips, where one fails and one day's volume looks wrong, how to set thresholds and severity, where checks belong, and the tools that package them.
Cross-Validation in Machine Learning: A Practical Guide
Cross-validation done properly: what cross_val_score does by default, how to pick the splitter your data actually needs (stratified, grouped or time-ordered), and a runnable demonstration of how preprocessing outside a pipeline turns random noise into an 85% score.
Pydantic Explained: Data Validation in Python
Pydantic v2 shown on one realistic job, validating a payment webhook: how models convert and check types, field constraints, custom field and model validators, the structured errors it reports all at once, strict mode, parsing and producing JSON, and validating lists with TypeAdapter.