Day 1

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

Day 2

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

Day 3

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