Explain how to implement MLOps pipeline for model deployment and monitoring.

MLOps integrates machine learning development with operations to automate model lifecycle management and ensure reliable production deployment.\n\n• CI/CD: Automated testing, model validation, deployment pipelines\n• Monitoring: Model performance, data drift, infrastructure metrics\n• Versioning: Model artifacts, data versions, experiment tracking\n• Infrastructure: Containerization, orchestration, auto-scaling\n\nExample: Use MLflow for experiment tracking, Docker for containerization, Kubernetes for orchestration. Implement automated retraining triggers, A/B testing framework, and comprehensive monitoring dashboards. Ensure model reproducibility and rollback capabilities.