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MLOps and production deployment
The unglamorous half that decides whether any of it survives.
What this actually involves
A model in a notebook is not a product. Production means versioned prompts, regression evaluation on every change, cost ceilings that hold, and enough observability that a bad output can be traced to the run that caused it.
- CI/CD for prompts, models, and retrieval indexes
- Continuous evaluation wired into the deploy pipeline
- Cost, latency, and quality monitoring with alerting
- Audit trails, PII handling, and governance documentation
Ready to move from AI experiments to AI in production?
Thirty minutes with a senior engineer. We assess the use case, name the risks, and tell you honestly whether we are the right fit.