A notebook that works once is not a product. Production ML needs data contracts, monitoring, and a rollback story.

Freeze the interface

Define input and output schemas early so product and engineering can build around a stable contract.

Evaluate continuously

Ship with offline metrics and online monitors. Drift happens; silence is the real failure mode.

Plan the fallback

Every model path needs a deterministic fallback when confidence drops or latency spikes.

LLM endpoints are slower and less deterministic than classic CRUD APIs. Design for streaming, partial failure, and clear versioning from day one.

Stream early

Users tolerate latency better when tokens arrive progressively.

Version prompts like code

Treat prompt and model changes as deployable artifacts with rollback.