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
Reducing Dependencies for Inference
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- Use multi-stage builds, Docker, 2024 (Docker) - This official documentation provides detailed guidance on implementing multi-stage Docker builds, a fundamental technique for creating leaner and more secure container images by separating build-time dependencies from runtime requirements.
- Best practices for writing Dockerfiles, Docker, 2024 (Docker) - A comprehensive guide from Docker on optimizing Dockerfile creation for smaller, more efficient, and more secure images, covering aspects like choosing base images, minimizing layers, and cleaning up unnecessary files.
- Distroless containers, GoogleContainerTools, 2024 (GoogleContainerTools) - This project repository explains the concept and implementation of Distroless images, which provide extremely minimal base images by including only the application and its runtime dependencies, significantly reducing image size and attack surface.
- Designing Machine Learning Systems: New Rules for an AI-Driven World, Chip Huyen, 2022 (O'Reilly Media) - A foundational book on MLOps and the engineering challenges of productionizing machine learning models, offering practical advice and architectural considerations relevant to efficient model deployment and containerization.