Book Image

Mastering Kubernetes - Fourth Edition

By : Gigi Sayfan
3.3 (3)
Book Image

Mastering Kubernetes - Fourth Edition

3.3 (3)
By: Gigi Sayfan

Overview of this book

The fourth edition of the bestseller Mastering Kubernetes includes the most recent tools and code to enable you to learn the latest features of Kubernetes 1.25. This book contains a thorough exploration of complex concepts and best practices to help you master the skills of designing and deploying large-scale distributed systems on Kubernetes clusters. You’ll learn how to run complex stateless and stateful microservices on Kubernetes, including advanced features such as horizontal pod autoscaling, rolling updates, resource quotas, and persistent storage backends. In addition, you’ll understand how to utilize serverless computing and service meshes. Further, two new chapters have been added. “Governing Kubernetes” covers the problem of policy management, how admission control addresses it, and how policy engines provide a powerful governance solution. “Running Kubernetes in Production” shows you what it takes to run Kubernetes at scale across multiple cloud providers, multiple geographical regions, and multiple clusters, and it also explains how to handle topics such as upgrades, capacity planning, dealing with cloud provider limits/quotas, and cost management. By the end of this Kubernetes book, you’ll have a strong understanding of, and hands-on experience with, a wide range of Kubernetes capabilities.
Table of Contents (21 chapters)
19
Other Books You May Enjoy
20
Index

Kubernetes and AI

AI is the hottest trend right now. Large language models (LLMs) and Generative Pre-trained Transforms (GPT) surprised most professionals with their capabilities. The release of ChatGPT 3.5 by OpenAI was a watershed moment. AI suddenly excels in areas that were considered strongholds of human intelligence, such as creative writing, painting, understanding, answering nuanced questions, and, of course, coding. My perspective is that advanced AI is the solution to the big data problem. We learned to collect a lot of data, but analyzing and extracting insights from the data is a difficult and labor-intensive process. AI seems like the right technology to digest all the data and automatically understand, summarize, and organize it into a useful form to be used by humans and other systems (most likely AI-based systems).

Let’s see why Kubernetes is such a great fit for AI workloads.

Kubernetes and AI synergy

Modern AI is all about deep learning networks...