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

Exploring the benefits of DevOps

DevOps is not just about toolsets. Say you have a tool available that can run unit tests for you. However, if the team has no culture of writing test cases, the tool would not be useful. DevOps is about how we work together on tasks that span across different teams. So, the three primary areas to focus on in DevOps are these:

  • People: Teams from multiple disciplines to achieve a common goal
  • Processes: The way teams work together
  • Technology: The tools that facilitate collaboration across different teams

DevOps is built on top of Agile development practices with the objective of streamlining the software development process. DevOps teams are cross-functional, and they have the autonomy to build software through continuous integration/continuous delivery (CI/CD). DevOps encourages teams to collaborate over a fast feedback loop to improve the efficiency and quality of the software being developed.

The following diagram illustrates...