MLOps Engineering on AWS (MLOE) – Outline

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