MLOps Engineering on AWS

Module 0: Welcome

• Course introduction

Module 1: Introduction to MLOps

• Machine learning operations
• Goals of MLOps
• Communication
• From DevOps to MLOps
• ML workflow
• Scope
• MLOps view of ML workflow
• MLOps cases

Module 2: MLOps Development

• Intro to build, train, and evaluate machine learning models
• MLOps security
• Automating
• Apache Airflow
• Kubernetes integration for MLOps
• Amazon SageMaker for MLOps
• Lab: Bring your own algorithm to an MLOps pipeline
• Demonstration: Amazon SageMaker
• Intro to build, train, and evaluate machine learning models
• Lab: Code and serve your ML model with AWS CodeBuild
• Activity: MLOps Action Plan Workbook

Module 3: MLOps Deployment

• Introduction to deployment operations
• Model packaging
• Inference
• Lab: Deploy your model to production
• SageMaker production variants
• Deployment strategies
• Deploying to the edge
• Lab: Conduct A/B testing
• Activity: MLOps Action Plan Workbook

Module 4: Model Monitoring and Operations

• Lab: Troubleshoot your pipeline
• The importance of monitoring
• Monitoring by design
• Lab: Monitor your ML model
• Human-in-the-loop
• Amazon SageMaker Model Monitor
• Demonstration: Amazon SageMaker Pipelines, Model Monitor, model registry, and Feature Store
• Solving the Problem(s)
• Activity: MLOps Action Plan Workbook

Module 5: Wrap-up

• Course review
• Activity: MLOps Action Plan Workbook
• Wrap-up