Machine learning development frequently encounters issues with environment setup, dependency conflicts, and ensuring reproducibility across different systems. Containerization, particularly using Docker, provides a structured approach to address these problems by packaging applications and their dependencies together.

This chapter establishes the groundwork by reviewing fundamental Docker concepts from the perspective of machine learning workflows. We will examine:

• The specific benefits of using containers for consistency and scalability in ML projects.
• Core Docker components: images, containers, and layers, as they relate to ML environments.
• Basic commands for managing the container lifecycle.
• An introduction to Dockerfiles for defining custom environments.
• The role of Docker registries (like Docker Hub) for sharing and obtaining images.

By the end of this chapter, you will understand why Docker is beneficial for ML and gain practical experience running pre-built ML images.