Module 1: Introduction to MLOps Processes People Technology Security and governance MLOps maturity model

Module 2: Initial MLOps: Experimentation Environments in SageMaker Studio Bringing MLOps to experimentation Setting up the ML experimentation environment Demonstration: Creating and Updating a Lifecycle Configuration for SageMaker Studio Hands-On Lab: Provisioning a SageMaker Studio Environment with the AWS Service Catalog Workbook: Initial MLOps

Module 3: Repeatable MLOps: Repositories Managing data for MLOps Version control of ML models Code repositories in ML

Module 4: Repeatable MLOps: Orchestration ML pipelines Demonstration: Using SageMaker Pipelines to Orchestrate Model Building Pipelines

Module 4: Repeatable MLOps: Orchestration (continued) End-to-end orchestration with AWS Step Functions Hands-On Lab: Automating a Workflow with Step Functions End-to-end orchestration with SageMaker Projects Demonstration: Standardizing an End-to-End ML Pipeline with SageMaker Projects Using third-party tools for repeatability Demonstration: Exploring Human-in-the-Loop During Inference Governance and security Demonstration: Exploring Security Best Practices for SageMaker Workbook: Repeatable MLOps

Module 5: Reliable MLOps: Scaling and Testing Scaling and multi-account strategies Testing and traffic-shifting Demonstration: Using SageMaker Inference Recommender Hands-On Lab: Testing Model Variants