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

Module 5: Reliable MLOps: Scaling and Testing (continued)

• Hands-On Lab: Shifting Traffic
• Workbook: Multi-account strategies

Module 6: Reliable MLOps: Monitoring

• The importance of monitoring in ML
• Hands-On Lab: Monitoring a Model for Data Drift
• Operations considerations for model monitoring
• Remediating problems identified by monitoring ML solutions
• Workbook: Reliable MLOps
• Hands-On Lab: Building and Troubleshooting an ML Pipeline