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
Designing Inference Services
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- Designing Machine Learning Systems: An Introduction to MLOps, Chip Huyen, 2022 (O'Reilly Media) - Covers fundamental concepts of designing and deploying ML systems, including model serving strategies, API design for inference, and operational considerations.
- Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, Martin Kleppmann, 2017 (O'Reilly Media) - Explores principles for building robust and scalable systems, relevant for understanding statelessness, synchronous vs. asynchronous patterns, and reliable data handling in inference services.
- Building Microservices: Designing Fine-Grained Systems, Sam Newman, 2021 (O'Reilly Media) - Provides architectural guidance for building microservices, applicable to containerized inference services, covering API contracts, communication patterns, and independent deployability.
- RESTful Web Services, Leonard Richardson, Sam Ruby, and David Thomas, 2007 (O'Reilly Media) - A foundational text on designing RESTful web APIs, essential for defining service interfaces, HTTP methods, and error handling for prediction endpoints.
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