Python for DevOps | Learn Ruthlessly Effective Automation

Alfredo Deza (u. a.)

ISBN 10: 149205769X / ISBN 13: 9781492057697

Published by O'Reilly Media, 2019

About this Item

Python for DevOps | Learn Ruthlessly Effective Automation | Alfredo Deza (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2019 | O'Reilly Media | EAN 9781492057697 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.

Seller Inventory # 116835983

Bibliographic Details

• Title: Python for DevOps | Learn Ruthlessly Effective Automation
• Publisher: O'Reilly Media
• Publication Date: 2019
• Language: English
• ISBN 10: 149205769X
• ISBN 13: 9781492057697
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 233W x 177H x 28D millimeters 233 millimeters width by 177 millimeters height by 28 millimeters depth
• Weight: 877 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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

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/.