Azure ML Workspace with Terraform: Your ML Platform on Azure 🔬

Azure Machine Learning workspace is the hub for all ML activities - experiments, models, endpoints, pipelines. It requires four dependent services. Here's how to provision the entire platform with Terraform including compute instances and clusters. In Series 1-3, we worked with managed AI services - AI Foundry for models, AI Search for RAG, Agent Service for orchestration. Series 5 shifts to custom ML - training your own models, deploying endpoints, managing features, and building CI/CD pipelines. It starts with an Azure Machine Learning workspace. The workspace is the top-level resource for all ML activities: experiments, datasets, models, compute targets, endpoints, and pipelines live here. Unlike a simple resource, the workspace requires four dependent services before it can be created: Storage Account, Key Vault, Application Insights, and Container Registry. Terraform provisions the entire stack. 🎯 🏗️ Workspace Architecture Component What It Does Workspace Central hub for ML expe