MLOps, evaluation & assurance
Connect experiments to controlled releases, meaningful evaluation and a practical operating model for AI systems.
What evidence should allow this version into production?
Define release evidence for the complete system, including data, model, prompts, retrieval and tools where relevant. Separate exploratory results from acceptance criteria. Make it possible to trace an observed behaviour to the configuration that produced it.
Build evaluation around important behaviours and failure conditions. Retain representative examples, versioned datasets and useful error categories. Review quality, latency, cost and operational constraints together rather than treating a single score as a release decision.
Connect evaluation to deployment gates, observability and incident response. Specify ownership for regressions, changes in data and unexpected outputs. Use controlled rollout and rollback to support learning without losing operational control.
What to take forward
- Versioned evaluation and release evidence
- Deployment gates and rollback design
- Monitoring and accountable operating model
What should I bring to a conversation?
Bring the decision you need to make, the constraints you already know and a small, non-confidential example. We can then agree whether a resource, a focused review or a more detailed discussion is the right next step.