“Mastering Docker for MLOps & Machine Learning: The Complete Zero-to-Hero Guide”

Docker has become a core tool in MLOps, offering consistency, scalability, and easy deployment of machine learning models across different environments. Here’s an all-inclusive guide that takes you from Docker novice to expert, with references to essential articles, books, and hands-on resources.

Part 1: Getting Started with Docker Basics

1. What is Docker? Why Use it for MLOps?

• Containerization 101: Docker is a platform for creating, deploying, and running applications in containers, providing a self-contained environment that includes your code and dependencies.
• Benefits for MLOps: Ensures consistency across development, testing, and production; enables scalability and simplifies deployment.
• Resources:
• Docker Documentation
• “Docker Deep Dive” by Nigel Poulton — A comprehensive introduction.

1. Setting Up Docker

• Installation: Install Docker Desktop on your local machine (Windows, macOS, or Linux).
• Basic Commands: Practice commands like docker run, docker pull, docker build, and docker ps to manage containers.
• Resources:
• Docker Installation Guide

Part 2: Docker Essentials for Machine Learning