MLOps: The Machine Learning Lifecycle in Production

A premium, advanced course on MLOps — the discipline of taking machine learning models through their full production lifecycle in 2026. You will learn what MLOps really is and how to assess team maturity, experiment tracking and reproducibility (MLflow, Weights & Biases), data and feature management with feature stores (Feast, Tecton), building training pipelines with orchestrators (Airflow, Kubeflow, Metaflow), model registries and versioning, CI/CD and testing for ML, model serving patterns (batch, online, streaming) with KServe, BentoML and Triton, deployment strategies (canary, shadow, A/B testing), production monitoring for data drift, concept drift and performance decay (Evidently, Prometheus, Grafana), automated retraining and continuous training, reproducibility and data lineage, GPU cost and FinOps, model governance with model cards and fairness monitoring, and how LLMOps differs from classic MLOps. Includes a strong focus on GDPR-compliant data handling, model documentation, bias monitoring, and a comprehensive final assessment. Educational content only.