Description

Introduction

Integrating AWS SageMaker with CI/CD for ML focuses on automating the machine learning lifecycle—from model development to deployment—by applying DevOps principles using AWS services. This course explores how to build Continuous Integration and Continuous Deployment (CI/CD) pipelines that streamline ML workflows, reduce manual effort, and improve model quality and delivery consistency. By leveraging tools like AWS CodePipeline, CodeBuild, and SageMaker Pipelines, you’ll learn how to automate training, testing, and deployment of models with agility and governance.

Prerequisites

Participants should have:

• A foundational understanding of machine learning and AWS SageMaker.

• Familiarity with CI/CD concepts and DevOps tools.

• Experience with Git, Python, and YAML.

• Working knowledge of AWS services such as S3, CodeCommit, CodeBuild, and CloudFormation.

Table of Contents

1. Overview of CI/CD in Machine Learning

   • 1.1 Introduction to MLOps and CI/CD

   • 1.2 Challenges of Integrating ML with CI/CD

   • 1.3 Benefits of Automation in the ML Lifecycle

2. AWS CI/CD Tools for ML Integration

   • 2.1 Introduction to AWS CodePipeline, CodeBuild, and CodeCommit

   • 2.2 Role of SageMaker Pipelines in Automation

   • 2.3 IAM Roles and Permissions for Pipeline Execution

3. Version Control for ML Projects

   • 3.1 Structuring Repositories for ML Projects

   • 3.2 Managing Code, Data, and Model Versions

   • 3.3 Using Git Hooks for Model Validation

4. Automating Model Training and Evaluation

   • 4.1 Triggering SageMaker Training Jobs via CodePipeline

   • 4.2 Incorporating Data Validation and Model Evaluation

   • 4.3 Storing and Registering Models with SageMaker Model Registry

5. Continuous Deployment of ML Models

   • 5.1 Setting Up Deployment Stages (Dev/Staging/Prod)

   • 5.2 Automating Endpoint Updates Using CodeBuild

   • 5.3 Blue/Green and Canary Deployments in SageMaker

6. Monitoring and Rollbacks

   • 6.1 Integrating CloudWatch and Model Monitor

   • 6.2 Configuring Alerts and Performance Metrics

   • 6.3 Automating Rollbacks on Deployment Failure

7. Hands-on Lab: Building a Full ML CI/CD Pipeline

   • 7.1 Setting Up CodeCommit Repository

   • 7.2 Defining and Launching a SageMaker Pipeline

   • 7.3 Deploying and Testing the Pipeline End-to-End

8. Best Practices and Governance

   • 8.1 CI/CD for Regulated Environments

   • 8.2 Audit Trails, Logging, and Compliance

   • 8.3 Securing Secrets and Credentials in Pipelines

Integrating SageMaker with CI/CD pipelines empowers ML teams to build scalable, reliable, and repeatable machine learning workflows. By automating the entire ML lifecycle—training, testing, deployment, and monitoring—you not only accelerate time to production but also enhance quality and governance. This course equips you with the tools and knowledge to establish robust ML delivery pipelines using the AWS ecosystem.