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
Why Containerize ML Projects?
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- What is a container?, Docker Documentation, 2024 (Docker Inc.) - Explains the fundamental concepts and benefits of containers, including portability and isolation.
- Building Machine Learning Powered Applications: Going from Idea to Product, J. Kalantri and Emmanuel Raj, 2021 (O'Reilly Media) - Discusses how containerization supports reproducibility, dependency management, and consistent deployment in MLOps workflows.
- MLOps principles: A survey of machine learning operations, practices, and tools, Rahul Singh, Praful Kumar, Pavan Kumar, Nivedita Kalra, Deepshikha Kalra, Satish Singh, Bhupesh Kumar, 2022 Journal of Advanced Computing and Communication Technologies, Vol. 3 (2) (Trade Science Inc.) DOI: 10.36548/jact.2022.2.001 - Surveys MLOps practices, highlighting containerization as a solution for environment management and reproducibility in ML projects.
- Docker for Data Scientists, Daniel J. Chen, 2020 (Packt Publishing) - Practical guide for data scientists on using Docker to manage environments, ensuring reproducibility and simplifying collaboration for ML projects.