How do you implement MLOps practices for scalable data science workflows?

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