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Implementing GitOps with Kubernetes

You're reading from   Implementing GitOps with Kubernetes Automate, manage, scale, and secure infrastructure and cloud-native applications on AWS and Azure

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781835884225
Length 444 pages
Edition 1st Edition
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Authors (2):
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Artem Lajko Artem Lajko
Author Profile Icon Artem Lajko
Artem Lajko
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Table of Contents (21) Chapters Close

Preface 1. Part 1: Understanding GitOps via Uncomplicated Orchestrations/ Kubernetes
2. Chapter 1: An Introduction to GitOps FREE CHAPTER 3. Chapter 2: Navigating Cloud-native Operations with GitOps 4. Chapter 3: Version Control and Integration with Git and GitHub 5. Chapter 4: Kubernetes with GitOps Tools 6. Part 2: Harnessing Advanced Orchestrations, Culture, and Control in GitOps Practices
7. Chapter 5: GitOps at Scale and Multitenancy 8. Chapter 6: GitOps Architectural Designs and Operational Control 9. Chapter 7: Cultural Transformation in IT for Embracing GitOps 10. Part 3: Hands-on Automating Infrastructure and CI/CD with GitOps
11. Chapter 8: GitOps with OpenShift 12. Chapter 9: GitOps for Azure and AWS Deployments 13. Chapter 10: GitOps for Infrastructure Automation – Terraform and Flux CD 14. Chapter 11: Deploying Real-World Projects with GitOps on Kubernetes 15. Part 4: Operational Excellence Through GitOps Best Practices
16. Chapter 12: Observability with GitOps 17. Chapter 13: Security with GitOps 18. Chapter 14: FinOps, Sustainability, AI, and Future Trends for GitOps 19. Index 20. Other Books You May Enjoy

Looking at GitOps and AI-driven automation

As the IT landscape becomes increasingly complex, especially when dissecting all aspects of Kubernetes in detail without the aid of GitOps or other tools, many layers accumulate, spanning from the operating system level through the network and up to public key infrastructures (PKIs). I notice more frequently that new employees in this field are finding it increasingly difficult to get started and to know where exactly to begin, especially when they are thrust into a project. Hence, there are practical AI tools available that can provide support in this area.

In this chapter, we will explore two tools. The first tool, Robusta.dev, serves as a troubleshooting co-pilot for developers, providing them with real-time insights and solutions. The second tool is a retrieval-augmented generation (RAG) model, a self-implemented middleware solution from iits, which can be used as an enhancement to support developers with a knowledge base. By combining...

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