This repository offers a 10-day syllabus for DevOps engineers to integrate Generative AI into their daily workflows, aiming to boost productivity through practical, hands-on exercises. It covers prompt engineering, local LLM deployment, AI-assisted scripting, AIOps, and AI agents for automation and analysis.

How It Works

The syllabus guides users through practical applications of LLMs for DevOps tasks. It emphasizes hands-on experience with tools like Ollama and APIs from providers like OpenAI and Mistral. Key techniques include prompt engineering (zero-shot, few-shot, Chain-of-Thought) and building AI agents using frameworks like CrewAI to automate complex operations such as generating infrastructure code, analyzing logs, and querying internal documentation.

Quick Start & Requirements

• Installation: Primarily involves setting up Python environments and potentially Docker for local LLM execution.
• Prerequisites: Python, Docker, cloud provider accounts (AWS mentioned), and potentially specific LLM models (e.g., Llama3).
• Resources: Local LLM execution may require significant CPU/GPU resources. API calls depend on service availability and potential costs.
• Links: No direct links to setup scripts or demos are provided in the syllabus itself.

Highlighted Details

• Practical application of AI for generating Bash scripts, Terraform, and CI/CD configurations.
• Hands-on experience with local LLM execution (Ollama, LM Studio) and API integrations.
• Focus on building AI agents for tasks like internal documentation Q&A and research analysis.
• Covers AIOps for log analysis and anomaly detection.

Maintenance & Community

• The repository is maintained by iam-veeramalla.
• No specific community channels (Discord, Slack) or roadmap details are provided in the syllabus.

Licensing & Compatibility

• The license is not specified in the provided text.

Limitations & Caveats

The syllabus outlines "Work in Progress" (WIP) for Day 9 (AI for Security & FinOps). Some hands-on exercises are described as "Mini-Challenges" or "Demos," implying varying levels of complexity and completion. The depth of theoretical AI/LLM concepts is explicitly stated as minimal.