MLOps Engineering on AWS

Course Modules

Day 1:

1. Module 1: Introduction to MLOps

   • Understanding MLOps and its significance.
   • Comparing DevOps and MLOps methodologies.
   • Evaluating security and governance requirements for ML use cases.
   • Setting up experimentation environments using Amazon SageMaker.
   • Hands-On Lab: Provisioning a SageMaker Studio Environment with the AWS Service Catalog.

2. 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.
   • Workbook Activity: Initial MLOps planning.

Day 2:

1. Module 3: Repeatable MLOps: Repositories

   • Managing data for MLOps.
   • Version control of ML models.
   • Utilizing code repositories in ML projects.
   • Lab: Integrating custom algorithms into an MLOps pipeline.
   • Group Activity: Developing an MLOps action plan.

2. Module 4: Repeatable MLOps: Orchestration

   • Building ML pipelines.
   • Automating workflows with Apache Airflow.
   • Integrating Kubernetes for MLOps.
   • Utilizing Amazon SageMaker for MLOps.
   • Demonstration: Using SageMaker Pipelines for orchestration.
   • Lab: Automating workflows with AWS Step Functions.

Day 3:

1. Module 5: MLOps Deployment

   • Understanding deployment operations.
   • Packaging models for deployment.
   • Implementing inference strategies.
   • Lab: Deploying models to production environments.
   • Exploring deployment strategies and their use cases.
   • Lab: Conducting A/B testing for model evaluation.

2. Module 6: Model Monitoring and Operations

   • Monitoring ML models in production.
   • Detecting data drift and performance issues.
   • Strategies for model retraining and continuous improvement.
   • Lab: Setting up monitoring and retraining pipelines.