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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
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David Ping
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Table of Contents (19) Chapters Close

Preface 1. Navigating the ML Lifecycle with ML Solutions Architecture 2. Exploring ML Business Use Cases FREE CHAPTER 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

Networking on Kubernetes

Kubernetes operates a flat private network among all the resources in a Kubernetes cluster. Within a cluster, all Pods can communicate with each other cluster-wide without a network address translation (NAT). Kubernetes gives each Pod its own cluster private IP address, which is the same IP address seen by the Pod itself and what others see it as. All containers inside a single Pod can reach each container’s port on the localhost. All nodes in a cluster have their individually assigned IP addresses as well and can communicate with all Pods without a NAT. The following figure shows the different IP assignments for Pods and nodes, and communication flows from different resources:

Figure 6.6 – IP assignments and communication flow

Figure 6.6: IP assignments and communication flow

Sometimes, you might need a set of Pods running the same application container (the container for an nginx application) for high availability and load balancing, for example. Instead of calling each Pod by its private...

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