MLOps bridges the gap between ML experimentation and production reliability. Core components: 1) Experiment tracking with MLflow: log parameters, metrics, artifacts for every experiment run. Enables reproducibility and comparison. mlflow.autolog() captures common framework metrics automatically. 2) Data versioning with DVC: version your datasets and model artifacts alongside code in Git. DVC stores data in S3/GCS while tracking pointers in Git - full reproducibility without storing large files in Git. 3) CI/CD for ML: GitHub Actions pipeline for ML - lint code, validate data schema with Great Expectations, run unit tests, train model on PR, compare metrics to baseline, only merge if metrics improve. 4) Feature store: Feast or Tecton compute features once, serve consistently in training and serving APIs. Eliminates training-serving skew. 5) Model registry: MLflow Model Registry for staging -> production promotion with approval workflows. 6) Monitoring: Evidently AI for data drift and model performance drift detection. Alert on input distribution shifts, prediction distribution changes, ground truth label drift. 7) Retraining triggers: schedule-based (monthly), metric-based (accuracy drops below threshold), drift-based (input distribution shift detected). MLOps maturity levels: Level 0 (manual), Level 1 (automated training), Level 2 (full pipeline automation with CT/CD).