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Python For DevOps: Learn Ruthlessly Effective Automation

Publisher: O’Reilly Media

Release Date: December 31st, 2019

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Build Status

Abstract

Much has changed in technology over the past decade. Data is hot, the cloud is ubiquitous, and many organizations need some form of automation. Throughout these transformations, Python has become one of the most popular languages in the world. This practical resource shows you how to use Python for everyday Linux systems administration tasks with today’s most useful DevOps tools, including Docker, Kubernetes, and Terraform.

Learning how to interact and automate with Linux is essential for millions of professionals. Python makes it much easier. With this book, you’ll learn how to develop software and solve problems using containers, as well as how to monitor, instrument, load-test, and operationalize your software. Looking for effective ways to “get stuff done” in Python? This is your guide.

Python foundations, including a brief introduction to the language How to automate text, write command-line tools, and automate the filesystem Linux utilities, package management, build systems, monitoring and instrumentation, and automated testing Cloud computing, infrastructure as code, Kubernetes, and serverless Machine learning operations and data engineering from a DevOps perspective Building, deploying, and operationalizing a machine learning project

Book Outline

Chapter 1: Python Essentials for DevOps

Chapter 2: Automating Files and the Filesystem

Chapter 3: Working with the Command Line

• Source Case Study
• GPU Programming Colab Notebook
• Notes

Chapter 4: Useful Linux Utilities

Chapter 5: Package Management

Chapter 6: Continuous Integration and Continuous Deployment

• Source
• Notes

Chapter 7: Monitoring and Logging

Chapter 8: Pytest for DevOps

Chapter 9: Cloud Computing

• Source
• Notes

Chapter 10: Infrastructure as Code

Chapter 11: Container Technologies: Docker and Docker Compose

Chapter 12: Container Orchestration: Kubernetes

Chapter 13: Serverless Technologies

Chapter 14: MLOps and Machine learning Engineering

• Source
• Notes

Chapter 15: Data Engineering

• Source

Chapter 16: DevOps War Stories and Interviews

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FAQ

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Addendum

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Noah Gift

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