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Python for Devops: Learn Ruthlessly Effective Automation
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
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
506 pages, ebook
Published December 12, 2019
109 people are currently reading
252 people want to read
About the author
Noah Gift
14 books13 followersNoah Gift is lecturer and consultant at UC Davis Graduate School of Management in the MSBA program, Northwestern's Master of Data Science program, and UC Berkeley Data Science program. He is also the founder of Pragmatic AI Labs. At Pragmatic AI Labs he provides consulting and training on Machine Learning and AI, and also develops AI SaaS products. At UC Davis, he is teaching graduate machine learning and consulting on Machine Learning and Cloud Architecture for students and faculty. He has published close to 100 technical publications including two books on subjects ranging from Pragmatic AI: An Introduction to Cloud-Based Machine Learning, First Edition to DevOps. He is also a certified AWS Solutions Architect and has an MBA from UC Davis, a M.S. in Computer Information Systems from Cal State Los Angeles, and a B.S. in Nutritional Science from Cal Poly San Luis Obispo. Professionally, Noah has approximately 20 years’ experience programming in Python and is a member of the Python Software Foundation. He has worked in roles ranging from CTO, General Manager, Consulting CTO and Cloud Architect. This experience has been with a wide variety of companies including ABC, Caltech, Sony Imageworks, Disney Feature Animation, Weta Digital, AT&T, Turner Studios and Linden Lab. In the last ten years, he has been responsible for shipping many new products at multiple companies that generated millions of dollars of revenue and had global scale. Currently he is consulting startups and other companies, on Machine Learning, Cloud Architecture and CTO level consulting via Pragmatic AI Labs
Ratings & Reviews
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Displaying 1 - 7 of 7 reviews
April 11, 2021
It could have been an absolute bomb, but it is not :(
My initial impression was stellar - it was supposed to be a book that presents how one can use Python to maximize the ROI on using some key DevOps tools/techniques. I imagined it would cover specialized toolkits/libraries/etc. And the book really started that way, e.g. how to quickly and easily craft Python CLI tools with a choice of 3 libraries. These were new for me, so I was so hooked on.
Unfortunately, the further into the book, the worse (it became). There's less and less stuff specific to Python, just an ultra-brief intro to particular tech (e.g. Terraform, Pulumi or K8s), a code sample (not necessarily in Python) w/o introducing the basic concepts or key idioms. And jump straight into the next topic, like a bunch of American tourists visiting whole Europe in 2 weeks.
This book could have been an incredible resource for people who have a general knowledge on modern system architectures, CI/CD and DevOps/SysOps in general, but who are not really proficient in Python ecosystem (apart of knowing the syntax). Unfortunately, for a reason I can't understand, the author has lost his way :(
My initial impression was stellar - it was supposed to be a book that presents how one can use Python to maximize the ROI on using some key DevOps tools/techniques. I imagined it would cover specialized toolkits/libraries/etc. And the book really started that way, e.g. how to quickly and easily craft Python CLI tools with a choice of 3 libraries. These were new for me, so I was so hooked on.
Unfortunately, the further into the book, the worse (it became). There's less and less stuff specific to Python, just an ultra-brief intro to particular tech (e.g. Terraform, Pulumi or K8s), a code sample (not necessarily in Python) w/o introducing the basic concepts or key idioms. And jump straight into the next topic, like a bunch of American tourists visiting whole Europe in 2 weeks.
This book could have been an incredible resource for people who have a general knowledge on modern system architectures, CI/CD and DevOps/SysOps in general, but who are not really proficient in Python ecosystem (apart of knowing the syntax). Unfortunately, for a reason I can't understand, the author has lost his way :(
December 29, 2019
Very eclectic tome spanning all aspects of python-driven DevOps automation.
Chapter 5 is the best introduction and walk-through of packaging (for PyPi, Debian and RPM) I have seen recently in print or on the web. Highly recommended!
Chapter 5 is the best introduction and walk-through of packaging (for PyPi, Debian and RPM) I have seen recently in print or on the web. Highly recommended!
August 7, 2020
I had to stop after the first few chapters. This book is full of mistakes, non sequiturs, and odd stories that seem really out of place. I really don't get how this got past the editors. There's some decent material in here and I'll likely reference parts of it later, but I definitely would not recommend a straight through read to anyone. Especially if you don't already know enough to spot the mistakes.
December 22, 2024
This book explains DevOps principle and describe related tools and how to utilize them for your Python application.
It covers all the principal DevOps practices: from testing to deploying on the cloud and monitoring, giving small code examples in each chapter.
It is normal for this kind of books that discuss a wide selection of topics for the coverage to lack in depth, as many of the presented chapter could deserve an entire book on their own. So, the most value can be taken from the book by noting down the described tools, use the examples in the book to see what the tool can do and try to apply it in your workflow.
What I would have liked to see is the code for end-to-end CI/CD pipelines including all principles and tools explained in the book. This is done partly in the relevant chapters, but it would have been nice to have a complete final example that could have taken the place of the last two chapters on MLOps and Big Data, as these are huge topics.
Overall the book could be a good addition to your DevOps collection. 7/10
It covers all the principal DevOps practices: from testing to deploying on the cloud and monitoring, giving small code examples in each chapter.
It is normal for this kind of books that discuss a wide selection of topics for the coverage to lack in depth, as many of the presented chapter could deserve an entire book on their own. So, the most value can be taken from the book by noting down the described tools, use the examples in the book to see what the tool can do and try to apply it in your workflow.
What I would have liked to see is the code for end-to-end CI/CD pipelines including all principles and tools explained in the book. This is done partly in the relevant chapters, but it would have been nice to have a complete final example that could have taken the place of the last two chapters on MLOps and Big Data, as these are huge topics.
Overall the book could be a good addition to your DevOps collection. 7/10
February 6, 2021
I didn’t finish this book or even read much of it but I did read sections and found it to be very thorough. I checked it out from the library, so had to return it after only a few weeks. But I can tell interested readers that it is very much a walk-you-though-it type book with lots of examples that you can duplicate - references to where to clone git repos, which docker images to pull from docker hub, etc.
It didn’t apply as much to what I am doing so I won’t “finish” it. But, if I were starting out in DevOps, this would be a book I would want to have!
It didn’t apply as much to what I am doing so I won’t “finish” it. But, if I were starting out in DevOps, this would be a book I would want to have!
January 13, 2024
В книге есть парочку хороших идей, но, в целом, она достаточно неудачная. Я повелся на многообещающее оглавление, однако автор надергал в каждую из глав весьма пестрый набор не всегда связанных между собой данных и привел не самые удачные примеры кода. Некоторые предлагаемые им модули, на первый взгляд, хороши, но при более детальном изучении замечаешь, что эти проекты почти не развиваются и не имеют большой поддержки. Поэтому советовать их для использования в производстве как-то не совсем целесообразно.
September 17, 2024
The title seems misleading, not so much python and too much blah blah about devops.
Displaying 1 - 7 of 7 reviews