Description

Introduction

Deploying and Managing Models on AWS SageMaker is a focused course for machine learning practitioners, data scientists, and DevOps engineers who want to operationalize ML models using AWS SageMaker. This course dives into deployment strategies, endpoint management, scaling, monitoring, and model version control—everything you need to reliably serve and manage models in production. Whether you’re deploying your first model or looking to streamline your ML workflows, this guide will help you take full advantage of SageMaker’s capabilities.

Prerequisites

To benefit from this course, learners should have:

• Basic experience with Python and Jupyter notebooks.

• Understanding of machine learning concepts (e.g., training, inference, evaluation).

• Familiarity with AWS fundamentals (S3, IAM, CloudWatch).

• A trained model ready for deployment on SageMaker (optional but ideal).

Table of Contents

1. Introduction to Model Deployment in SageMaker

   • 1.1 Overview of Deployment Options

   • 1.2 Real-Time Inference vs. Batch Transform

   • 1.3 Use Cases and Deployment Architecture

2. Preparing a Trained Model for Deployment

   • 2.1 Saving and Registering a Model Artifact

   • 2.2 Creating a Model in SageMaker

   • 2.3 Understanding Model Registry and SageMaker Model Packages

3. Deploying Models to Real-Time Endpoints

   • 3.1 Creating and Configuring Endpoints

   • 3.2 Deploying with Built-in or Custom Containers

   • 3.3 Testing the Endpoint with Sample Payloads

4. Batch Transform for Offline Inference

   • 4.1 Setting Up a Batch Transform Job

   • 4.2 Handling Large Datasets and Output Storage

   • 4.3 Comparing Batch and Real-Time Inference

5. Model Monitoring and Logging

   • 5.1 Enabling Model Monitoring with SageMaker Model Monitor

   • 5.2 Analyzing Drift and Data Quality

   • 5.3 Using CloudWatch for Logs and Metrics

6. Scaling and Optimization

   • 6.1 Auto Scaling SageMaker Endpoints

   • 6.2 Multi-Model Endpoints (MME) for Cost Efficiency

   • 6.3 Optimizing Model Performance and Latency

7. Version Control and Model Management

   • 7.1 Using SageMaker Model Registry

   • 7.2 Tracking and Comparing Model Versions

   • 7.3 Promoting Models from Staging to Production

8. Security and Cost Management

   • 8.1 Managing IAM Roles and Policies for Deployment

   • 8.2 Securing Endpoints and Data

   • 8.3 Monitoring and Controlling Costs

With AWS SageMaker, deploying and managing machine learning models is streamlined and production-ready. This course has equipped you with the knowledge to deploy models through real-time endpoints or batch jobs, monitor their health, manage versions, and optimize for scale and cost. You’re now prepared to maintain reliable ML services in production and support evolving business needs with confidence.