Kubeflow Operations Guide

Preface

What Is in This Book?

This book focuses on the DevOps and MLOps sides of deploying and operating Kubeflow. The authors feel that this is compelling and relevant content for today’s practicing DevOps/MLOps teams as this sector is still changing. Many machine learning platforms today take different approaches to the architecture and solution space of managing machine learning workflows. The difficulty of considering all aspects of operating a machine learning platform is where this story kicks off in Chapter 1: “Where are we today and what do we need to be thinking about from ground zero for machine learning platforms?”

This book starts by taking you through today’s machine learning infrastructure landscape and explaining the challenges and trade-offs faced by many enterprise teams today. We then go on to outline the core principles needed to support the full life cycle of machine learning operations and explain how Kubernetes solves some of the issues that arise. We’ll further show the remaining functional gaps in how Kubernetes fits into the MLOps picture, and how Kubeflow functions to complete the picture.

This book has three major parts. The first section (Chapters 1, 2, and 3) focuses on understanding the core concepts of Kubeflow and its architecture.

Chapter 1 covers machine learning architecture concerns, such as “Why is Kubernetes compelling here?” and “What does Kubeflow add beyond Kubernetes?” It includes a discussion of understanding today’s machine learning ...