Python for DevOps: Learn Ruthlessly Effective Automation

Python for DevOps: Learn Ruthlessly Effective Automation
By 作者: Noah Gift - Kennedy Behrman - Alfredo Deza - Grig Gheorghiu
ISBN-10 书号: 149205769X
ISBN-13 书号: 9781492057697
Edition 版本: 1
Release Finelybook 出版日期: 2019-12-31
pages 页数: (506 )

The Book Description robot was collected from Amazon and arranged by Finelybook

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
1.Python Essentials for DevOps
2.Automating Files and the Filesystem
3.Working with the Command Line
4.Useful Linux Utilities
5.Package Management
6.Continuous Integration and Continuous Deployment
7.Monitoring and Logging
8.Pytest for DevOps
9.Cloud Computing
10.Infrastructure as Code
11.Container Technologies:Docker and Docker Compose
12.Container Orchestration:Kubernetes
13.Serverless Technologies
14.MLOps and Machine learning Engineering
15.Data Engineering
16.DevOps War Stories and Interviews

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