MLOPS / 02
ML data & deployment pipeline
A governed production path from point-in-time feature data to CI-gated model promotion, with the lineage to explain every prediction.
point-in-timefeature correctness
CI-gatedpromotion path
end-to-endmodel lineage
01 / THE CHALLENGEMake the repeatable path
Make the repeatable path
the default path.
Model teams had good experiments but an incomplete path to production. Training data quality, model promotion, and downstream traceability needed to become platform concerns rather than individual heroics.
02 / THE ARCHITECTURESystems that stay
Systems that stay
legible under load.
Versioned feature and training datasets land on Delta with point-in-time correctness and pre-promotion quality gates. MLflow tracks experiments and registers models. Unity Catalog governs inputs and outputs, while CI validates changes before multi-environment promotion. Monte Carlo monitors upstream freshness, volume, and schema so drift surfaces before model behaviour degrades.
ML workloads inherit the same quality, access, and deployment discipline as the data platform.