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
Packaging Models Inside Images vs. Volumes
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- Use volumes, Docker Documentation, 2024 (Docker) - Official guide to Docker volumes, explaining their purpose, usage, and how they provide persistent storage for containers, which is essential for external model loading.
- Best practices for writing Dockerfiles, Docker Documentation, 2024 - Provides guidelines for optimizing Docker images, including minimizing size and build times, which is relevant to the increased image size disadvantage of embedding models.
- Introducing MLOps: How to go from Model to Production, Mark Treveil, Nicolas Omont, Aurélien Géron, Clemens Mewald, Michel Tessier, Alexey Grigorev, 2022 (O'Reilly Media) - A comprehensive book on MLOps practices that includes discussions on model serving, deployment strategies, and managing model artifacts in production environments.
- MLOps: A guide to operations for machine learning, Google Cloud Documentation, 2024 (Google Cloud) - Google Cloud's official MLOps guide, covering principles and practices for deploying and managing ML systems, including considerations for packaging and serving models with containerization.