This technical course focuses on operationalizing machine learning models and managing AI systems in production environments. Students learn CI/CD for ML, model versioning, automated testing, monitoring and observability, and scalable deployment architectures. The curriculum covers containerization, orchestration, model serving, performance optimization, and incident response. Participants gain hands-on experience implementing MLOps practices that ensure reliable model performance, enable rapid iteration, maintain model quality over time, and bridge the gap between data science experimentation and production-grade AI systems.