Reliable ML begins before the model
A model can perform well in a notebook and still fail in production. Often the failure starts upstream, where data changes quietly and assumptions remain undocumented.
Start with contracts
Define expected fields, ranges, freshness, ownership, and failure behaviour. A contract makes assumptions observable and testable.
Make the path visible
Track lineage from source to feature to prediction. Version data, code, configuration, and artifacts together.
Monitor the whole system
Latency, missingness, drift, cost, and feedback quality are part of model performance.