Enterprise AI MLOps and Monitoring

This unit covers end-to-end machine learning operations and data practices for deploying and maintaining enterprise AI in the cloud. Students learn AI/ML frameworks, CI/CD for models, real-time and batch monitoring, generative AI best practices, cloud ML services (including Google AI/ML), feature engineering, and workbook templates to plan and assess enterprise AI implementations.

Learning Objectives

• Analyze AI/ML frameworks and cloud provider service stacks to determine appropriate machine learning services and deployment patterns for enterprise use cases
• Apply MLOps principles to design and document CI/CD pipelines, model deployment plans, and automated monitoring workflows using provided workbook templates
• Demonstrate implementation of real-time and batch monitoring strategies and tools to detect model performance degradation, data drift, and operational issues
• Evaluate generative AI risks, best practices, and governance considerations and produce an enterprise AI platform setup checklist and future roadmap