Description

Introduction

SageMaker MLOps: Automating Model Deployment and Monitoring is designed for ML engineers, DevOps professionals, and data scientists aiming to operationalize machine learning workflows at scale. This course explores how AWS SageMaker supports MLOps practices such as CI/CD, model versioning, automated deployment, and continuous monitoring. Learn how to streamline the entire ML lifecycle—from model training to production deployment and ongoing performance monitoring—using the integrated tools and services in SageMaker.

Prerequisites

Participants should have:

• Working knowledge of machine learning workflows and SageMaker.

• Familiarity with CI/CD concepts and DevOps pipelines.

• Experience with Python and Jupyter Notebooks.

• Basic understanding of AWS services like S3, Lambda, and CloudWatch.

Table of Contents

1. Introduction to MLOps with SageMaker

   • 1.1 What is MLOps and Why It Matters

   • 1.2 SageMaker Tools for MLOps

   • 1.3 Lifecycle of a Production ML Model

2. Model Packaging and Registry

   • 2.1 Using SageMaker Model Registry

   • 2.2 Versioning and Metadata Tracking

   • 2.3 Promoting Models from Staging to Production

3. Automating Model Deployment

   • 3.1 Creating CI/CD Pipelines for ML with SageMaker Projects

   • 3.2 Integrating CodePipeline and CodeBuild

   • 3.3 Blue/Green and Canary Deployments in SageMaker

4. Infrastructure as Code for ML Workflows

   • 4.1 Using AWS CloudFormation and CDK with SageMaker

   • 4.2 Automating Endpoint Creation and Configuration

   • 4.3 Parameterizing and Reusing Deployment Templates

5. Model Monitoring and Drift Detection

   • 5.1 Setting Up SageMaker Model Monitor

   • 5.2 Capturing Input/Output Data and Predictions

   • 5.3 Configuring Alerts for Performance and Data Drift

6. Automated Retraining and Feedback Loops

   • 6.1 Defining Triggers for Retraining Pipelines

   • 6.2 Scheduling Batch Retraining with SageMaker Pipelines

   • 6.3 Integrating Monitoring Results into Automation

7. Security, Governance, and Compliance

   • 7.1 Managing IAM Roles and Access Control

   • 7.2 Logging, Audit Trails, and Model Lineage

   • 7.3 Ensuring Compliance with Automated Checks

8. Real-World Use Case: End-to-End MLOps Pipeline

   • 8.1 From Model Development to Deployment

   • 8.2 Monitoring and Alerting in Production

   • 8.3 Implementing Continuous Improvement

By mastering SageMaker MLOps, you gain the tools to bring structure, repeatability, and scalability to your machine learning workflows. Automation of deployment, monitoring, and retraining ensures your models stay accurate and production-ready over time. Whether working in a regulated environment or simply seeking to increase operational efficiency, SageMaker’s MLOps capabilities enable fast, secure, and reliable ML delivery.