Feature Store

2 pages

Feature Engineering concept
Feature operations (missing value handling MNAR/MAR/MCAR, scaling, discretisation, categorical encoding/hashing trick, feature crossing, positional embeddings); data leakage causes and detection; feature importance (top 10 = 50% importance); feature generalisation (coverage × distribution overlap); best practices (split by time, scale after split)
MLOps Infrastructure and Tooling concept
Four-layer MLOps stack (storage/compute, resource management, ML platform, dev environment); cloud repatriation; Docker + Kubernetes; workflow orchestration tools (Airflow/Argo/Prefect/Metaflow); model store (8 artifact types); feature store (management/computation/consistency, eliminates training-serving skew); build vs buy framework