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Hands-On Python for DevOps

You're reading from   Hands-On Python for DevOps Leverage Python's native libraries to streamline your workflow and save time with automation

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Product type Paperback
Published in Mar 2024
Publisher Packt
ISBN-13 9781835081167
Length 220 pages
Edition 1st Edition
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Author (1):
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Ankur Roy Ankur Roy
Author Profile Icon Ankur Roy
Ankur Roy
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Table of Contents (19) Chapters Close

Preface 1. Part 1: Introduction to DevOps and role of Python in DevOps
2. Chapter 1: Introducing DevOps Principles FREE CHAPTER 3. Chapter 2: Talking about Python 4. Chapter 3: The Simplest Ways to Start Using DevOps in Python Immediately 5. Chapter 4: Provisioning Resources 6. Part 2: Sample Implementations of Python in DevOps
7. Chapter 5: Manipulating Resources 8. Chapter 6: Security and DevSecOps with Python 9. Chapter 7: Automating Tasks 10. Chapter 8: Understanding Event-Driven Architecture 11. Chapter 9: Using Python for CI/CD Pipelines 12. Part 3: Let’s Go Further, Let’s Build Bigger
13. Chapter 10: Common DevOps Use Cases in Some of the Biggest Companies in the World 14. Chapter 11: MLOps and DataOps 15. Chapter 12: How Python Integrates with IaC Concepts 16. Chapter 13: The Tools to Take Your DevOps to the Next Level 17. Index 18. Other Books You May Enjoy

MLOps and DataOps

The future will soon be a thing of the past.

– George Carlin

I was going to use a bit of generative AI to write the content of this chapter but that would make it more of an autobiography than a technical book, since then the generative AI would become a co-author. I will try to be a very objective observer and do some justice to this hot topic. I hope this will give me a favorable position in a possible future ruled by AI. There are many reasons why there has been such great progress in the field of machine learning and AI in the past century: Noam Chomsky, Alan Turing, the creation of computers themselves, science fiction novels, and man’s eternal longing for new life in the universe. But in the past few years, the delivery of these concepts as viable products and services has required the DevOps touch. After all, how else do you think ChatGPT manages to instantly answer your four-paragraph questions in about five seconds and does it for a million...

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