Introduction
The practical handbook for infrastructure engineers
“You don’t need to be a data scientist to work with AI — but you do need to understand how it runs, scales, and is observed.”
About this project
As someone who spent years in infrastructure before transitioning into AI, I experienced firsthand how challenging it can be to bridge the gap between systems, compute, and machine learning. This repository AI for Infra Pros, was born from that journey. It documents the exact steps, labs, and concepts I wish I had when starting out, helping other infrastructure engineers confidently navigate their AI learning path. Each chapter connects real infrastructure knowledge to AI foundations, tools, and workloads on Azure and beyond.
Overview
AI for Infra Pros is a practical and technical handbook designed to help infrastructure, cloud, and DevOps professionals understand and apply Artificial Intelligence in a secure, efficient, and scalable way.
This repository combines foundational knowledge, best practices, and real-world examples to turn infrastructure expertise into a competitive advantage in the AI era.
What you’ll find
AI fundamentals explained from an infrastructure perspective
Architecture and automation models using Bicep, Terraform, and YAML
Hands-on mini-labs with AKS, GPU VMs, and Azure Machine Learning
Strategies for monitoring, security, and resilience in AI workloads
Visual glossary comparing key Infrastructure vs. AI concepts
An AI Adoption Framework tailored for technical teams
Chapter list
Below is a quick list of all chapters included in this handbook. Each chapter is self-contained and can be read independently:
Chapter 1 – AI fundamentals: link
Chapter 2 – Data: The fuel of AI: link
Chapter 3 – Infrastructure and compute for AI: link
Chapter 4 – IaC and automation: link
Chapter 5 – Monitoring and observability: link
Chapter 6 – Security in AI environments: link
Chapter 7 – AI use cases for infrastructure engineers: link
Chapter 8 – AI adoption framework: link
Chapter 9 – Azure OpenAI: TPM, RPM, and PTU: link
Chapter 10 – Visual glossary: link
How to navigate
Each chapter is designed to be independent and complementary. If you want to start quickly:
Understand how AI connects to Infrastructure
Build an AI environment
Measure and observe AI workloads
Ensure security and resilience
Get hands-on experience
Translate terms and concepts
Repository structure
ai-for-infra-pros/
├── docs/
│ ├── chapters/
│ │ ├── 01-introduction.md
│ │ ├── 02-data.md
│ │ ├── 03-compute.md
│ │ ├── 04-iac.md
│ │ ├── 05-monitoring.md
│ │ ├── 06-security.md
│ │ ├── 07-use-cases.md
│ │ ├── 08-adoption-framework.md
│ │ ├── 09-azure-openai-tpm-ptu.md
│ │ └── 10-visual-glossary.md
│ ├── extras/
│ │ ├── labs/
│ │ │ ├── bicep-vm-gpu/
│ │ │ │ └── README.md
│ │ │ ├── terraform-aks-gpu/
│ │ │ │ └── README.md
│ │ │ └── yaml-inference-api/
│ │ │ └── README.md
│ │ ├── case-studies.md
│ │ ├── cheatsheets.md
│ │ └── technical-faq.md
│ ├── images/
│ │ ├── infrastructure-flow.png
│ │ ├── model-life-cycle.png
│ │ └── relationship-tpm-qps-cost.png
│ └── README.md
├── README.md
└── SUMMARY.mdTarget audience
Professionals in:
Infrastructure and Cloud (Azure, AWS, GCP)
DevOps and SRE
Solutions Architecture
Security and Governance
Data Engineering professionals who want to understand the infrastructure side of AI
Mission
To turn infrastructure knowledge into an advantage in the era of Artificial Intelligence. To show that you don’t need to be a data scientist to work with AI — but you do need to understand how it runs, scales, and is observed.
Extra resources
Credits
Created by Ricardo Martins 📍 Principal Solutions Engineer @ Microsoft 📖 Author of Azure Governance Made Simple and Linux Hackathon 🌐 rmmartins.com
“AI needs infrastructure. And infrastructure needs to understand AI.”
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