Docker and Containerization for ML Projects
Chapter 1: Docker Concepts for Machine Learning
Why Containerize ML Projects?
Docker Image Fundamentals
Container Lifecycle Management
Introduction to Dockerfiles
Docker Registries and Repositories
Hands-on practical: Running Pre-built ML Images
Quiz for Chapter 1
Chapter 2: Building Custom ML Environments with Dockerfiles
Structuring your Dockerfile
Choosing the Right Base Image
Managing Python Dependencies (pip)
Managing Python Dependencies (Conda)
Working with Environment Variables
Copying Code and Artifacts
Setting the Working Directory and Entrypoint
Hands-on practical: Build a Scikit-learn Environment
Quiz for Chapter 2
Chapter 3: Managing Data and Models in Containers
Understanding Container Storage
Using Bind Mounts for Development
Using Docker Volumes for Persistence
Comparing Bind Mounts and Volumes
Accessing Cloud Storage from Containers
Packaging Models Inside Images vs. Volumes
Hands-on practical: Mounting Datasets and Saving Models
Quiz for Chapter 3
Chapter 4: Containerizing ML Training Workflows
Structuring Training Scripts for Containers
Passing Configuration and Hyperparameters
Running Training Jobs with
docker runManaging Training Logs
GPU Acceleration for Training
Introduction to Docker Compose for Training Stacks
Hands-on practical: Containerize and Run a Training Script
Quiz for Chapter 4
Chapter 5: Containerizing ML Models for Inference
Designing Inference Services
Building Inference APIs (Flask/FastAPI)
Optimizing Image Size: Multi-Stage Builds
Reducing Dependencies for Inference
Exposing Ports for API Access
Health Checks for Inference Containers
Hands-on practical: Containerize a Simple Inference API
Quiz for Chapter 5
Chapter 6: Streamlining Workflows with Docker Compose
Introduction to Docker Compose
Defining Services in
docker-compose.ymlNetworking Between Containers
Using Volumes with Compose
Environment Variables in Compose
Building Images with Compose
Common ML Stack Examples (e.g., API + DB)
Hands-on practical: Develop an ML App with Compose
Quiz for Chapter 6
Accessing Cloud Storage from Containers
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- IAM roles for Amazon EC2, Amazon Web Services, 2024 (Amazon Web Services) - Explains how to use IAM roles with EC2 instances to grant permissions to applications, including those in Docker containers, via the instance metadata service, enhancing security by avoiding explicit credential management.
- Authenticating to Google Cloud, Google Cloud, 2025 (Google Cloud) - Provides an overview of authentication methods for Google Cloud, including service accounts and Default Application Credentials, which are essential for secure access from containerized applications on GCE/GKE.
- What are managed identities for Azure resources?, Microsoft Azure, 2024 (Microsoft) - Describes Azure's managed identities, a secure way for applications running on Azure VMs or managed container services to authenticate to Azure services without needing to manage credentials.
- Boto3 Documentation - S3 client, Amazon Web Services, 2024 (Amazon Web Services) - Provides detailed API reference and examples for interacting with Amazon S3 using the boto3 Python SDK, covering operations like downloading and uploading objects from within a container.