Operationalizing Enterprise AI

This unit focuses on operational frameworks and practical practices for deploying, monitoring, and governing AI/ML in the cloud. Students will learn MLOps automation (CI/CD), feature engineering workflows, monitoring strategies (real-time vs. batch), and generative AI best practices while using workbook templates and reviewing major cloud ML services.

Learning Objectives

• Analyze enterprise AI/ML frameworks and major cloud ML service stacks (including the Google AI/ML services stack and generative AI platforms) to recommend an architecture for model deployment
• Apply MLOps automation principles to design a CI/CD pipeline and complete an enterprise AI cloud platform setup checklist and model deployment plan using provided workbook templates
• Demonstrate data processing and feature engineering workflows by developing a feature engineering plan and populating the data processing & feature engineering workflow template for a sample project
• Evaluate monitoring strategies and generative AI best practices to create a model monitoring and governance plan that distinguishes real-time vs. batch monitoring needs, selects appropriate tooling, and incorporates risk/ethical controls