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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

Google Cloud use case – MLB and AFL

If there is one thing that you should know about me, it is that I am a massive sports fan. I am, and I love learning about new sports just as much as following the old ones that I have followed for years.

I have been following Major League Baseball (MLB) for years, and in those years, baseball has always been the sport of analytics. Most modern teams in their team selection are driven by analytics and the performance of players is measured through statistical analysis of a number of metrics that are collected when they play their games. You may have seen the film Moneyball (or read the book by Michael Lewis), which chronicles the introduction of statistical methods in the choice of baseball players and how they led to the success of the Oakland Athletics baseball team in the late 90s and early 2000s.

In the MLB, one of the results of the introduction of analytical data is the analysis of the gameplay itself and the time taken to finish...

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