azureml-telemetry

import os import mlflow from azure.ai.ml import MLClient from azure.identity import DefaultAzureCredential # NOTE: This quickstart assumes you have an Azure Machine Learning workspace configured. # Replace with your actual subscription, resource group, and workspace name. # Set these environment variables or replace directly in the code for actual execution. subscription_id = os.environ.get('AZURE_SUBSCRIPTION_ID', 'your-subscription-id') resource_group = os.environ.get('AZURE_RESOURCE_GROUP', 'your-resource-group') workspace_name = os.environ.get('AZURE_ML_WORKSPACE_NAME', 'your-ml-workspace') # Authenticate and get MLClient try: ml_client = MLClient( DefaultAzureCredential(), subscription_id, resource_group, workspace_name ) print(f"Connected to Azure ML workspace: {ml_client.workspace_name}") except Exception as e: print(f"Could not connect to Azure ML workspace. Please ensure your credentials and workspace details are correct: {e}") print("Skipping MLflow example as workspace connection failed.") exit() # Set the MLflow tracking URI to point to the Azure Machine Learning backend # This ensures metrics and artifacts are logged to your workspace. mlflow.set_tracking_uri(ml_client.workspaces.get(name=workspace_name).mlflow_tracking_uri) # Start an MLflow run to log custom metrics with mlflow.start_run() as run: print(f"MLflow run ID: {run.info.run_id}") # Log a simple custom metric mlflow.log_metric("custom_accuracy", 0.95) mlflow.log_metric("custom_loss", 0.05) print("Logged custom_accuracy and custom_loss metrics.") # Log a parameter mlflow.log_param("model_type", "linear_regression") print("Logged model_type parameter.") # Simulate a loop and log multiple metric values for i in range(5): mlflow.log_metric("iteration_accuracy", 0.95 + i * 0.001, step=i) print("Logged iteration_accuracy over steps.") print("Telemetry logged successfully via MLflow to Azure ML.")