This repository contains the source code and materials for the O'Reilly book "Python For DevOps: Learn Ruthlessly Effective Automation." It targets engineers and system administrators seeking to leverage Python for automating Linux tasks, cloud infrastructure, and MLOps workflows. The book provides practical guidance on using Python with modern DevOps tools like Docker, Kubernetes, and Terraform.

How It Works

The book's approach is to demonstrate Python's utility across the DevOps lifecycle. It covers core Python concepts relevant to system administration, then progresses to automating text, command-line tools, and filesystem operations. Subsequent chapters delve into Linux utilities, package management, CI/CD, monitoring, testing, cloud computing, Infrastructure as Code (IaC) with Terraform, containerization with Docker and Kubernetes, serverless technologies, and MLOps/data engineering from a DevOps perspective.

Quick Start & Requirements

• Source code is available for download from GitHub.
• The book requires Python and familiarity with Linux environments. Specific tool requirements (Docker, Kubernetes, Terraform) are detailed within the book's chapters.
• Links to buy physical/Kindle copies, read online, and access source code are provided.

Highlighted Details

• Covers end-to-end ML engineering and MLOps from a DevOps viewpoint.
• Includes practical case studies and "DevOps War Stories and Interviews."
• Demonstrates automation with contemporary tools like Docker, Kubernetes, and Terraform.
• Offers chapters on cloud computing and serverless technologies.

Maintenance & Community

• The book is published by O'Reilly Media.
• Feedback can be provided by creating tickets in the repository.
• Pragmatic AI Labs offers related courses and maintains a Discord community.
• Authors include Noah Gift, Kennedy Behrman, and Grig Gheorghiu.

Licensing & Compatibility

• The repository's license is not explicitly stated in the README.
• Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The README does not specify the license under which the source code is distributed, which may impact commercial use or redistribution. There is also no mention of specific version requirements for Python or other tools, which could lead to compatibility issues.