Responsible MLOps

Practice responsible MLOps so RAI artefacts are produced by the engineering workflow rather than retrofitted from documentation. Learn data versioning paired with dataset cards, experiment tracking paired with model cards, model registry with RAI metadata (intended use, eval results, fairness, robustness, energy), deployment with RAI gates (no deploy without an approved RAI review and current model card), and the run-time RAI observability stack (drift, fairness over time, prompt-injection alerts, harmful-output rate).