data-engineering

To build machine learning data pipelines, you can construct workflows that collect data from diverse sources like APIs and databases, validate data integrity using Great Expectations, and manage storage patterns such as data lakes.

Data versioning with DVC tracks and manages changes in your machine learning datasets. It integrates into your data pipeline to ensure reliable storage and reproducible model training by maintaining dataset history.

The best way to validate data quality in ML pipelines is using Great Expectations. It implements checks to ensure data integrity, verifying that ingested data from sources like APIs meets your defined schema expectations.

Yes, you can collect data from APIs for your feature store. The pipeline connects to various sources including APIs and files, gathering data to organize into structured storage patterns like feature stores or vector stores.

Recommended storage patterns for ML data infrastructure include data lakes, feature stores, and vector stores. These patterns organize collected data efficiently to ensure reliable access for model training.