With a grasp of Docker concepts, this chapter focuses on building custom container images tailored for Machine Learning projects using Dockerfile instructions. You will learn to structure a Dockerfile for clarity and efficiency, select appropriate base images (like official Python or CUDA-enabled images), manage complex Python dependencies with tools such as pip and Conda, integrate your project code and artifacts into the image, and define the container's runtime behavior using WORKDIR, ENTRYPOINT, and CMD. The goal is to create consistent, reproducible environments needed for reliable ML development and execution.
2.1 Structuring your Dockerfile
2.2 Choosing the Right Base Image
2.3 Managing Python Dependencies (pip)
2.4 Managing Python Dependencies (Conda)
2.5 Working with Environment Variables
2.6 Copying Code and Artifacts
2.7 Setting the Working Directory and Entrypoint
2.8 Hands-on practical: Build a Scikit-learn Environment
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