Ml Ops Engineer

You're getting a full MLOps stack here: model deployment workflows, drift detection with KS tests, FastAPI serving endpoints with health checks, Kubernetes manifests with HPA rules, Feast feature store integration, and MLflow experiment tracking. The maturity model is helpful for figuring out where you are (manual notebooks vs. full CI/CD) and what to tackle next. The code is production-grade, complete with resource limits, rollback procedures, and alert thresholds. If you're moving past the "train in a notebook, pray in production" phase, this gives you the scaffolding for proper ML infrastructure. The monitoring section alone (latency percentiles, prediction drift, error rates) will save you from some painful incidents.