Machine Learning Model Deployment Guide

Build a comprehensive ML model deployment guide for [Company/Team]. Include: 1) Model packaging — containerization (Docker), model serialization (pickle, ONNX, TorchScript), dependency management, versioning strategy. 2) Serving infrastructure — real-time serving (FastAPI, TensorFlow Serving, Triton) vs batch predictions, auto-scaling configuration, GPU vs CPU cost optimization, A/B testing infrastructure. 3) Deployment patterns — shadow deployment, canary rollout, blue-green deployment, feature flag integration. When to use each. 4) Monitoring — model performance metrics (accuracy, precision, recall, AUC drift), data drift detection (PSI, KS test, KL divergence), feature drift, prediction distribution monitoring. 5) Alerting — define alert thresholds for model degradation, automated retraining triggers, human-in-the-loop escalation. 6) Retraining pipeline — automated retraining schedule, data validation checks, champion-challenger model comparison, automated rollback criteria. 7) Governance — model registry (MLflow, Weights & Biases), model cards, lineage tracking, audit trail for predictions. 8) Cost management — inference cost monitoring, model optimization (quantization, pruning, distillation), right-sizing compute. 9) Documentation — model documentation template, API documentation, runbook for common issues, on-call guide.