As you move from local development to production systems, ensuring your machine learning code runs consistently across different environments becomes a significant challenge. Differences in operating systems, library versions, or hardware drivers can lead to errors that are difficult to debug. This chapter addresses this problem by introducing tools for creating portable and scalable ML workflows.

You will begin with Docker, the industry standard for containerization. We will cover how to package an application, its dependencies, and its configurations into a single, isolated unit called a container. You will learn to write a Dockerfile specifically for an ML application, including the necessary CUDA and Python libraries. From there, we will move to orchestration with Kubernetes. You will see how Kubernetes automates the deployment, scaling, and management of containerized applications, making it an effective platform for handling complex ML workloads. The sections will detail how to manage GPU resources within a Kubernetes cluster and introduce Kubeflow for building structured ML pipelines. The chapter concludes with a hands-on exercise where you will containerize and deploy a model-serving application.