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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 run

Managing 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.yml

Networking 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

Quiz

Chapter: Managing Data and Models in Containers

Test your understanding and practice the concepts from this chapter

Quiz Instructions

This quiz contains 12 questions to help you practice.
You need to score at least 70% to pass.
Attempts: Unlimited.
Your highest score will be kept.
Please attempt this quiz without assistance; however, feel free to refer to the chapter notes or use a code interpreter if needed.
Complete all chapter quizzes to earn a course completion certificate. Learn more

Question Format

The questions are designed to be engaging, focusing on understanding, application, and interpretation rather than rote memorization. Expect scenario-based problems that test your ability to apply what you've learned.

Attempts

Best scores and quiz attempts will appear.

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