Description

Introduction

Custom Algorithms and Containers in AWS SageMaker explores how to go beyond built-in models and leverage your own ML algorithms using Docker containers. This course empowers machine learning engineers and data scientists to package, deploy, and scale custom training and inference logic in SageMaker. You’ll learn to build and register custom containers, integrate with SageMaker’s training and hosting APIs, and adhere to best practices for secure and efficient model lifecycle management.

Prerequisites

Participants should have:

• Familiarity with Python and ML libraries (e.g., scikit-learn, TensorFlow, or PyTorch).

• Understanding of Docker fundamentals and containerization.

• Experience with AWS SageMaker and basic AWS CLI usage.

• Working knowledge of cloud storage (e.g., S3) and Linux-based development environments.

Table of Contents

1. Introduction to Custom Algorithms in SageMaker
    1.1 Why Use Custom Algorithms
    1.2 Options: Script Mode vs Bring Your Own Container (BYOC)
    1.3 Overview of SageMaker Training and Inference Architecture

2. Building Your Own Algorithm with Script Mode
    2.1 Packaging Python Scripts for SageMaker
    2.2 Using the Estimator API with Custom Scripts
    2.3 Custom Training and Inference Entry Points

3. Creating Custom Docker Containers
    3.1 Setting Up a Docker Environment
    3.2 Creating Dockerfile for SageMaker Training
    3.3 Creating Dockerfile for SageMaker Inference

4. Pushing Containers to Amazon ECR
    4.1 Creating ECR Repositories
    4.2 Building and Tagging the Image
    4.3 Authenticating and Pushing to ECR

5. Running Custom Containers with SageMaker
    5.1 Using the FrameworkProcessor and Estimator Classes
    5.2 Launching Training Jobs with Custom Containers
    5.3 Deploying Custom Inference Endpoints

6. Testing and Debugging Containers
    6.1 Local Testing with SageMaker Local Mode
    6.2 Handling Input/Output Channels
    6.3 Debugging Logs and Errors

7. Advanced Topics
    7.1 Multi-Container Models (Inference Pipelines)
    7.2 Handling Large Dependencies and External Libraries
    7.3 Optimizing for Cost and Speed

8. Security and Compliance
    8.1 Controlling Access to ECR and SageMaker
    8.2 Using Secrets and Environment Variables
    8.3 Ensuring Container Security and Patch Management

9. Real-World Project
    9.1 Building a Custom NLP Model Container
    9.2 Training on SageMaker and Deploying to Endpoint
    9.3 Monitoring and Updating the Container

Custom algorithms and containers in SageMaker unlock flexibility, scalability, and portability for ML workloads. This course equips you to deploy any model, regardless of framework or architecture, within the powerful SageMaker ecosystem. Whether you need custom dependencies, optimized runtime, or proprietary logic, SageMaker’s container support provides the infrastructure to scale with confidence.