Detailed Course Outline
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