Book Image

Machine Learning on Kubernetes

By : Faisal Masood, Ross Brigoli
Book Image

Machine Learning on Kubernetes

By: Faisal Masood, Ross Brigoli

Overview of this book

MLOps is an emerging field that aims to bring repeatability, automation, and standardization of the software engineering domain to data science and machine learning engineering. By implementing MLOps with Kubernetes, data scientists, IT professionals, and data engineers can collaborate and build machine learning solutions that deliver business value for their organization. You'll begin by understanding the different components of a machine learning project. Then, you'll design and build a practical end-to-end machine learning project using open source software. As you progress, you'll understand the basics of MLOps and the value it can bring to machine learning projects. You will also gain experience in building, configuring, and using an open source, containerized machine learning platform. In later chapters, you will prepare data, build and deploy machine learning models, and automate workflow tasks using the same platform. Finally, the exercises in this book will help you get hands-on experience in Kubernetes and open source tools, such as JupyterHub, MLflow, and Airflow. By the end of this book, you'll have learned how to effectively build, train, and deploy a machine learning model using the machine learning platform you built.
Table of Contents (16 chapters)
1
Part 1: The Challenges of Adopting ML and Understanding MLOps (What and Why)
5
Part 2: The Building Blocks of an MLOps Platform and How to Build One on Kubernetes
10
Part 3: How to Use the MLOps Platform and Build a Full End-to-End Project Using the New Platform

Chapter 3: Exploring Kubernetes

Now that you have seen that Kubernetes will form the basis of your machine learning (ML) platform, it's logical to refresh your knowledge of the underlying bedrock of our solution. Though there are many resources available on the internet on this topic of Kubernetes, we will briefly discuss the role of Kubernetes in the cloud era and the flexibility it provides for building solutions. You will also learn about Operators in Kubernetes and how they help simplify the installation and operation of Kubernetes workloads. By the end of this chapter, you will have built a running minikube instance either in your local machine or in the cloud. This is a single-node Kubernetes cluster that you will use as the base infrastructure to build and run the ML platform.

In this particular order, we will cover the following topics:

  • Exploring Kubernetes major components
  • Becoming cloud-agnostic through Kubernetes
  • Understanding Operators
  • Setting...