How do you implement MLOps for production machine learning systems?

MLOps applies DevOps practices to machine learning, managing the lifecycle from experimentation to production. ML lifecycle: data management (versioning, validation, feature engineering), experimentation (model training, hyperparameter tuning, evaluation), deployment (model packaging, serving, monitoring), and maintenance (retraining, drift detection, model retirement). Implementation: use ML platforms (MLflow for experiment tracking and model registry, Weights & Biases for experiment visualization, or Vertex AI/SageMaker for end-to-end managed platforms). Experiment tracking: log every training run with hyperparameters, metrics, data version, code version, and model artifacts. Compare runs to identify best configurations. Enable reproducibility by recording the full environment. Data versioning: track which data was used for each training run (DVC for Git-like data versioning, or Delta Lake/Iceberg for lakehouse-based versioning), implement data validation in training pipelines (Great Expectations), and maintain data lineage from source to features. Model registry: centralized catalog of all trained models with metadata (version, performance metrics, training data, deployment status). Implement promotion workflow: experimental to staging to production, with required validation at each stage. CI/CD for ML: continuous training pipeline triggered by data changes or schedule, automated evaluation against acceptance criteria, automated model registration on passing evaluation, and automated deployment with canary rollout. Feature store: centralize feature computation and serving (Feast, Tecton), ensure training-serving consistency, and enable feature reuse across models. Monitoring: track model performance in production (accuracy, calibration), detect data drift (input distribution changes), detect concept drift (relationship between inputs and outputs changes), and implement automated retraining triggers when drift exceeds thresholds. Governance: maintain model cards for documentation, implement approval workflows for production deployment, track model lineage (data, code, config that produced each model), and support audit and compliance requirements.