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The Machine Learning Solutions Architect Handbook

You're reading from   The Machine Learning Solutions Architect Handbook Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI

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Product type Paperback
Published in Apr 2024
Publisher Packt
ISBN-13 9781805122500
Length 602 pages
Edition 2nd Edition
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Author (1):
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David Ping David Ping
Author Profile Icon David Ping
David Ping
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Table of Contents (19) Chapters Close

Preface 1. Navigating the ML Lifecycle with ML Solutions Architecture FREE CHAPTER 2. Exploring ML Business Use Cases 3. Exploring ML Algorithms 4. Data Management for ML 5. Exploring Open-Source ML Libraries 6. Kubernetes Container Orchestration Infrastructure Management 7. Open-Source ML Platforms 8. Building a Data Science Environment Using AWS ML Services 9. Designing an Enterprise ML Architecture with AWS ML Services 10. Advanced ML Engineering 11. Building ML Solutions with AWS AI Services 12. AI Risk Management 13. Bias, Explainability, Privacy, and Adversarial Attacks 14. Charting the Course of Your ML Journey 15. Navigating the Generative AI Project Lifecycle 16. Designing Generative AI Platforms and Solutions 17. Other Books You May Enjoy
18. Index

Overview of Kubernetes and its core concepts

Managing and orchestrating a small number of containers and containerized applications manually within a compute environment can be relatively manageable. However, as the number of containers and servers grows, the task becomes increasingly complex. Enter Kubernetes, a powerful open-source system specifically designed to address these challenges. First introduced in 2014, Kubernetes (commonly abbreviated to K8s, derived from replacing “ubernete” with the digit 8) offers a comprehensive solution for efficiently managing containers at scale across clusters of servers.

Kubernetes follows a distributed architecture consisting of a master node and multiple worker nodes within a server cluster. Here, a server cluster refers to the set of machines and resources that Kubernetes manages, and a node is a single physical or virtual machine in the cluster.

The master node, often referred to as the control plane, assumes the primary...

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