Azure Machine Learning

Overview

Azure Machine Learning is a cloud-based platform for building, training, and deploying machine learning models at scale.

Real-life Use Cases

• Cloud Architect: Design end-to-end ML pipelines for production workloads.

• DevOps Engineer: Automate model deployment and monitoring.

Terraform Example

resource "azurerm_machine_learning_workspace" "main" {
  name                = "mlworkspace"
  location            = azurerm_resource_group.main.location
  resource_group_name = azurerm_resource_group.main.name
}

Bicep Example

resource mlWorkspace 'Microsoft.MachineLearningServices/workspaces@2023-04-01' = {
  name: 'mlworkspace'
  location: resourceGroup().location
  properties: {}
}

Azure CLI Example

az ml workspace create --name mlworkspace --resource-group my-rg --location westeurope

Best Practices

• Use pipelines for reproducibility.

• Monitor model drift and retrain as needed.

Common Pitfalls

• Not securing endpoints.

• Underestimating storage needs for training data.

Joke: Why did the ML model go to Azure ML? To get some cloud training!