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https://github.com/paiml/python_devops_book

[Book-2020] Python For DevOps: Learn Ruthlessly Effective Automation
https://github.com/paiml/python_devops_book

automation book devops oreilly python ruthless

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[Book-2020] Python For DevOps: Learn Ruthlessly Effective Automation

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README 

        
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# Python For DevOps: Learn Ruthlessly Effective Automation

#### Publisher:  [O'Reilly Media](http://shop.oreilly.com/product/0636920274902.do)

#### Release Date:  December 31st, 2019



![Python for Unix and Linux System Administration](https://www.noahgift.com/img/python_devops.png)

* [Buy a Physical Copy from Amazon](https://www.amazon.com/Python-DevOps-Ruthlessly-Effective-Automation-dp-149205769X/dp/149205769X)

* [Buy a Kindle Copy from Amazon](https://www.amazon.com/Python-DevOps-Ruthlessly-Effective-Automation-ebook-dp-B082P97LDW/dp/B082P97LDW)

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* [Read Online](http://shop.oreilly.com/product/0636920274902.do)

* [Download Source Code from Github](https://github.com/paiml/python_devops_book)

* [Python for DevOps Website](https://pythondevops.com/)



#### Build Status

[![CircleCI](https://circleci.com/gh/paiml/python_devops_book.svg?style=svg)](https://circleci.com/gh/paiml/python_devops_book)



### 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](https://github.com/noahgift/nuclear_powered_command_line_tools)

* [GPU Programming Colab Notebook](https://github.com/noahgift/cloud-data-analysis-at-scale/blob/master/GPU_Programming.ipynb)

* [Notes](https://paiml.github.io/python_devops_book/notes/chap3)



### Chapter 4: Useful Linux Utilities

### Chapter 5:  Package Management

### Chapter 6:  Continuous Integration and Continuous Deployment



* [Source](https://github.com/noahgift/hugotools)

* [Notes](https://paiml.github.io/python_devops_book/notes/chap6)



### Chapter 7: Monitoring and Logging

### Chapter 8: Pytest for DevOps

### Chapter 9: Cloud Computing



* [Source](https://github.com/paiml/python_devops_book/tree/master/src/chap9)

* [Notes](https://paiml.github.io/python_devops_book/notes/chap9)



### 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](https://github.com/paiml/python_devops_book/tree/master/src/chap14) 

* [Notes](https://paiml.github.io/python_devops_book/notes/chap14)



### Chapter 15: Data Engineering



* [Source](https://github.com/noahgift/awslambda)



### Chapter 16: DevOps War Stories and Interviews



## Got Feedback?



If you have any suggestions as the book is being developed please [create a ticket](https://github.com/paiml/python_devops_book/issues) and let us know!  Thanks for helping make this an incredible book.



## FAQ



A list of Frequently Asked Questions about the book:



* [FAQ](https://paiml.github.io/python_devops_book/docs/faq)



## Addendum



Updates on new material post book release.



* [Addendum](https://paiml.github.io/python_devops_book/docs/addendum.md)



## Contact Authors



### Noah Gift



* [Noah Gift Website](https://noahgift.com)

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* [Watch Videos by Noah Gift](https://www.noahgift.com/videos/latest/)

* [Take the Foundations of Data Engineering Specialization](https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke)

* [Take the Specialization: Building Cloud Computing Solutions at Scale Specialization](https://www.coursera.org/learn/cloud-computing-foundations-duke?specialization=building-cloud-computing-solutions-at-scale)



#### 📺 Latest YouTube Videos



![YouTube Channel Subscribers](https://img.shields.io/youtube/channel/subscribers/UCNDfiL0D1LUeKWAkRE1xO5Q?label=YouTube%20Subscribers&style=social)



- [DevOps: من تويوتا إلى السحابة](https://www.youtube.com/watch?v=sJw3o8owdIg)

- [DevOps演进：从丰田到云计算](https://www.youtube.com/watch?v=DI7wLMSlU3E)

- [Evolución DevOps: De Toyota a la Nube](https://www.youtube.com/watch?v=54p5vov7Anw)

- [DevOps Evolution: From Toyota to the Cloud](https://www.youtube.com/watch?v=DGXuTKu5IYA)

- [What is Amazon Bedrock?](https://www.youtube.com/watch?v=TbwtTj7C5Qk)

- [Writing Clean Testable Code](https://www.youtube.com/watch?v=VFWkCB3kadg)

- [The Little Data Thief Who Could: Chapter Nine-Bay Area Billionairism Manifesto](https://www.youtube.com/watch?v=kh4t375s8pI)



### Kennedy Behrman



* [Kennedy Behrman Linkedin](https://www.linkedin.com/in/kennedybehrman/)



### Alfredo Deza



* [Alfredo Deza](https://www.alfredodeza.com/)



### Grig Gheorghiu



* [Grig Gheorghiu](https://www.linkedin.com/in/ggheorghiu/)



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