🧪Mini-Labs Overview

Welcome to the hands-on labs section of the AI for Infra Pros — The Practical Handbook for Infrastructure Engineers. Each lab demonstrates how to apply the infrastructure concepts from the book in real-world Azure environments.

Lab index

Lab	Description	Technologies
Terraform AKS GPU Cluster	Provision an Azure Kubernetes Service cluster with a dedicated GPU node pool for AI workloads.	Terraform, AKS, GPU, IaC
Bicep VM with GPU	Deploy a single GPU-enabled VM using Azure Bicep to host AI inference workloads.	Bicep, Azure CLI, NVidia Drivers
YAML Inference API (Azure ML)	Publish a trained model as an inference endpoint using Azure Machine Learning and YAML configuration.	Azure ML, YAML, CLI, REST API

Prerequisites

Before running any of the labs:

• Have an active Azure Subscription

• Install the latest Azure CLI

• Install Terraform and/or Bicep depending on the lab

• Ensure GPU quotas are available in your target region

• Have sufficient permissions (Owner or Contributor on the Resource Group)

Lab workflow

All labs follow a similar structure:

1. Provision infrastructure (VM, AKS, or AML workspace)

2. Configure access, security, and monitoring

3. Deploy models or containers for inference

4. Validate performance and connectivity

5. Clean up resources to avoid unnecessary costs

Recommendations

• Use East US or West Europe regions — they typically have better GPU availability.

• Always tag resources with project and owner names for tracking.

• Store deployment logs for auditing and rollback.

• For production-grade deployments, add Private Endpoints and Azure Policy validation.

Cleanup reminder

After finishing a lab, remember to delete the created resources to prevent billing surprises:

az group delete --name <your-resource-group> --yes --no-wait

References

• Azure Machine Learning Documentation

• Terraform on Azure

• Bicep Language Reference

• Azure AI Infrastructure Overview

“You don’t scale AI with PowerPoint — you scale it with Infrastructure as Code.”