Designing Scalable Data Pipelines for Machine Learning Applications

Short summary

A detailed guide on designing production-grade data pipelines for ML, covering batch vs streaming tradeoffs, lambda architecture, and the critical problem of training-serving skew. Feature stores like Feast and Tecton ensure consistent feature definitions between training and inference. Data versioning tools (DVC, LakeFS, Delta Lake) and lineage tracking (OpenLineage, Marquez) provide reproducibility and auditability for compliance and debugging.

• •Batch vs streaming pipeline choice shapes everything downstream; lambda architecture combines both
• •Feature stores prevent training-serving skew by defining features once for training and inference
• •Data versioning and lineage tracking are essential for ML reproducibility and compliance

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