ml-system-design

Prevent training-serving skew by enforcing data contracts and maintaining strict parity between training and inference pipelines. Separating feature, training, and inference pipelines ensures consistent data transformations across both environments.

The FTI pipeline architecture pattern separates feature, training, and inference pipelines into distinct components. This separation enforces reproducibility by design and maintains clear boundaries for monitoring data drift across ML systems.

Design a real-time inference service by integrating a feature store to serve pre-computed features consistently. Apply FTI pipelines to separate feature generation from training and inference, preventing data leakage during live predictions.

You need a feature store when managing feature parity between training and serving environments. It centralizes feature definitions, prevents training-serving skew, and provides version control for reproducibility across ML teams.

Common ML pipeline anti-patterns include coupling training and inference code, lacking data contracts, and ignoring reproducibility gaps. Architecture reviews help identify these issues by enforcing strict separation and monitoring for drift.

The ML system design approach applies to both batch and real-time pipelines by enforcing data contracts and pipeline separation. Architecture patterns like FTI ensure reproducibility and prevent data leakage regardless of the processing mode.