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Python for DevOps: Learn Ruthlessly Effective Automation 1st Edition
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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
- ISBN-10149205769X
- ISBN-13978-1492057697
- Edition1st
- PublisherO'Reilly Media
- Publication dateJanuary 7, 2020
- LanguageEnglish
- Dimensions7.01 x 1.02 x 9.17 inches
- Print length506 pages
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Sharing the knowledge of experts
O'Reilly's mission is to change the world by sharing the knowledge of innovators. For over 40 years, we've inspired companies and individuals to do new things (and do them better) by providing the skills and understanding that are necessary for success.
Our customers are hungry to build the innovations that propel the world forward. And we help them do just that.
From the Publisher
From the Preface
This book is useful in any order. You can randomly open any chapter you like, and you should be able to find something helpful to apply to your job. If you are an experienced Python programmer, you may want to skim Chapter 1. Likewise, if you are interested in war stories, case studies, and interviews, you may want to read the Chapter 16 first.
Conceptual Topics
The content is broken up into several conceptual topics. The first group is Python Foundations, and it covers a brief introduction to the language as well as automating text, writing command-line tools, and automating the file system.
Next up is Operations, which includes useful Linux utilities, package management, build systems, monitoring and instrumentation, and automated testing. These are all essential topics to master to become a competent DevOps practitioner.
Cloud Foundations are in the next section, and there are chapters on Cloud Computing, Infrastructure as Code, Kubernetes, and Serverless. There is currently a crisis in the software industry around finding enough talent trained in the Cloud. Mastering this section will pay immediate dividends to both your salary and your career.
Next up is the Data section. Machine Learning Operations and Data Engineering are both covered from the perspective of DevOps.There is also a full soup to nuts machine learning project walkthrough that takes you through the building, deploying, and operationalizing of a machine learning model using Flask, Sklearn, Docker, and Kubernetes.
The last section is Chapter 16 on case studies, interviews.
What Does DevOps Mean to the Authors?
Many abstract concepts in the software industry are hard to define precisely. Cloud Computing, Agile, and Big Data are good examples of topics that can have many definitions depending on whom you talk to. Instead of strictly defining what DevOps is, let’s use some phrases that show evidence DevOps is occurring:
- Two-way collaboration between Development and Operation teams.
- Turnaround of Ops tasks in minutes to hours, not days to weeks.
- Strong involvement from developers; otherwise, it’s back to Devs versus Ops.
- Operations people need development skills—at least Bash and Python.
- Developer people need operational skills—their responsibilities don’t end with writing the code, but with deploying the system to production and monitoring alerts.
- Automation, automation, automation: you can’t accurately automate without Dev skills, and you can’t correctly automate without Ops skills
- Ideally: self-service for developers, at least in terms of deploying code.
- Can be achieved via CI/CD pipelines.
- GitOps.
- Bidirectional everything between Development and Operations (tooling, knowledge, etc.).
- Constant collaboration in design, implementation, deployment—and yes, automation—can’t be successful without cooperation.
- If it isn’t automated, it’s broken.
- Cultural: Hierarchy < Process.
- Microservices > Monolithic.
- The continuous deployment system is the heart and soul of the software team.
- There are no superheroes.
- Continuous delivery isn’t an option; it is a mandate.
Editorial Reviews
About the Author
Noah Gift is a lecturer and consultant at UC Davis Graduate School of Management in the MSBA program. Professionally, Noah has approximately 20 years’ experience programming in Python and is a member of the Python Software Foundation. He has worked for a variety of companies in roles ranging from CTO, general manager, consulting CTO, and cloud architect. Currently, he is consulting start-ups and other companies on machine learning and cloud architecture and is doing CTO-level consulting via Noah Gift Consulting. He has published close to 100 technical publications including two books on subjects ranging from cloud machine learning to DevOps. He is also a certified AWS Solutions Architect. Noah has an MBA from the University of California, Davis; an MS in computer information systems from California State University, Los Angeles; and a BS in nutritional science from Cal Poly, San Luis Obispo. You can find more about Noah by following him on Github (https://github.com/noahgift/), visiting http://noahgift.com, or connecting with him on https://www.linkedin.com/in/noahgift/.
Kennedy Behrman is a veteran consultant specializing in architecting and implementing cloud solutions for early-stage startups. He has both undergraduate and graduate degrees from the University of Pennsylvania, including an MS in Computer Information Technology and post-graduate work in the Computer Graphics and Game Programming program.
He is experienced in data engineering, data science, AWS solutions, and engineering management, and has acted as a technical editor on a number of python and data science-related publications. As a Data Scientist, he helped develop a proprietary growth hacking machine learning algorithm for a startup that led to the exponential growth of the platform. Afterward, he then hired and managed a Data Science team that supported this technology. Additional to that experience, he has been active in the Python language for close to 15 years including giving talks at user groups, writing articles, and serving as technical editor to many publications.
Alfredo Deza is a passionate software engineer, avid open source developer, Vim plugin author, photographer, and former Olympic athlete. He has given several lectures around the world about Open Source Software, personal development, and professional sports. He has rebuilt company infrastructure, designed shared storage, and replaced complex build systems, always in search of efficient and resilient environments. With a strong belief in testing and documentation, he continues to drive robust development practices wherever he is.
As a passionate knowledge-craving developer Alfredo can be found giving presentations in local groups about Python, file systems and storage, system administration, and professional sports.
Grig Gheorghiu has worked for the last 13 years as a programmer, research lab manager, system/network/security architect, and most recently as a software test engineer. Grig is the founder of the Southern California Python Interest Group, as well as a member of the Agile Alliance and of the xpsocal user group. He holds an MS degree in Computer Science from USC. Grig blogs fairly regularly on agile testing topics at agiletesting.blogspot.com.
Product details
- Publisher : O'Reilly Media; 1st edition (January 7, 2020)
- Language : English
- Paperback : 506 pages
- ISBN-10 : 149205769X
- ISBN-13 : 978-1492057697
- Item Weight : 1.76 pounds
- Dimensions : 7.01 x 1.02 x 9.17 inches
- Best Sellers Rank: #342,173 in Books (See Top 100 in Books)
- #16 in Unix Shell
- #430 in Python Programming
- #555 in Software Development (Books)
- Customer Reviews:
About the authors

Noah Gift is the founder of Pragmatic AI Labs. Noah Gift lectures at MSDS, at Northwestern, Duke MIDS Graduate Data Science Program, and the Graduate Data Science program at UC Berkeley and the UC Davis Graduate School of Management MSBA program, and UNC Charlotte Data Science Initiative. He is teaching and designing graduate machine learning, A.I., Data Science courses, and consulting on Machine Learning and Cloud Architecture for students and faculty. These responsibilities include leading a multi-cloud certification initiative for students.
Noah is a Python Software Foundation Fellow, and AWS ML Hero. He currently holds the following industry certifications for AWS: AWS Subject Matter Expert (SME) on Machine Learning, AWS Certified Solutions Architect, and AWS Certified Machine Learning Specialist, AWS Certified Big Data Specialist, AWS Academy Accredited Instructor, AWS Faculty Ambassador. He also is certified on both the Google and Azure platform: Google Certified Professional Cloud Architect, Certified Microsoft MTA on Python. He has published over 100 technical publications including multiple books on subjects ranging from Cloud Machine Learning to DevOps. Publications appear in Forbes, IBM, Red Hat, Microsoft, O’Reilly, Pearson, Udacity, Coursera, datascience.com, and DataCamp. Workshops and Talks around the world for organizations including NASA, PayPal, PyCon, Strata, O’Reilly Software Architecture Conference, and FooCamp. As an SME on Machine Learning for AWS, he helped created the AWS Machine Learning certification.
He has worked in roles ranging from CTO, General Manager, Consulting CTO, Consulting Chief Data Scientist, and Cloud Architect. This experience has been with a wide variety of companies: ABC, Caltech, Sony Imageworks, Disney Feature Animation, Weta Digital, AT&T, Turner Studios, and Linden Lab, and industries: Television, Film, Games, SaaS, Sports, Telecommunications. He has film credits in many major motion pictures for technical work, including Avatar, Spider-Man 3, and Superman Returns.
He has been responsible for shipping many new products at multiple companies that generated millions of dollars of revenue and had a global scale. Currently, he is consulting startups and other companies, on Machine Learning, Cloud Architecture, and CTO level consulting as the founder of Pragmatic A.I. Labs.

Alfredo Deza is a passionate software engineer, avid open source developer, Vim plugin author, photographer, and former Olympic athlete. He has given several lectures around the world about Open Source Software, personal development, and professional sports. He has rebuilt company infrastructure, designed shared storage, and replaced complex build systems, always in search of efficient and resilient environments. With a strong belief in testing and documentation, he continues to drive robust development practices wherever he is.
As a spirited knowledge-craving developer Alfredo can be found giving presentations in local groups about Python, file systems and storage, system administration, and professional sports.

Discover more of the author’s books, see similar authors, read author blogs and more

Kennedy Behrman is a veteran consultant and author, specializing in architecting and implementing cloud solutions for early-stage startups. Experienced in data engineering, data science, AWS solutions, and engineering management.
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They try to cover too many areas and lack depth. They clearly don’t know what an ASIC is when stating that a GPU is a type of ASIC (in that case, so is a CPU), their “encryption” section includes hashing (not encryption) and recommends md5 for password hashing and handrolled RSA from a library which is literally called “hazardous materials”, with zero notice that these are terrible ideas that nobody should use.
I’m not finished but this is only worth a skim for some useful library mentions. I wouldn’t pay from my own pocket for it.
I found it very interesting that you get 4 different author's perspectives in one book. There are also a lot of great examples that are ideal for general use (not way too hard like a typical text book).
This book is an absolute must-have for your office. Especially if you have a software focus, you're going to want to have a copy on hand.
Overall I would definitely recommend
Top reviews from other countries
It is mostly a mish-mash of different concepts.
The first chapters mostly address introductory topics like data types (e.g. lists tuples dictionaries ) plus functions for handling data (e.g. open, with/open/as functions and so on) which can be found in other books as well. Besides, always for data processing, for a basic usage of Pandas and dataframes, it's better to check somewhere else.
For the chapter on Linux, I use mostly Ubuntu & Arch, and for a basic introduction to the shell, it's way better to use Richard Blum's Linux Command Line & Shell Scripting Bible.
As for ML OPs, it's way better to check Geron's Hands-on-ML textbook (and other advanced textbooks on the same topics)
The section on Container Technologies(Docker) and Orchestration (Kubernetes) doesn't add a lot more than you can already find elsewhere. Nigel Poulton's Docker can be a starting point
Finally, I'm not an expert on Serverless Technologies & AWS Lambdas, and there seems to be some value in reading Chapter 13 . Still, the big picture is not entirely clear for me. I believe it would be better to integrate it with other sources, or to look directly for other specific references on this topic as well
In short: I wouldn't spend time reading it
I'd recommend this book if you are a seasoned developer and would like a quick reference of the useful parts of the language.
After I finish reading through the whole book I'll update this review with a more complete opinion.
Sin embargo, el libro explica muy bien los temas.
Al leer varias veces los capítulos, las cosas resultan más claras.













