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Azure Machine Learning (AzureML) provides a comprehensive, end‑to‑end platform for building, training, and deploying ML models at scale. Whether you're a data scientist, ML engineer, or developer, AzureML offers tools that simplify the entire workflow.

Key Features

• Automated ML: AutoML helps you build high‑quality models with minimal code.
• Designer: Drag‑and‑drop interface for building pipelines without writing code.
• ML Ops: Deploy, monitor, and manage models in production.
• Integration: Works seamlessly with Azure Databricks, Azure Synapse, and GitHub.

Quick Start

Follow these steps to train a simple regression model:

import azureml.core
from azureml.core import Workspace, Experiment, Dataset
from azureml.train.sklearn import SKLearn
# Connect to workspace
ws = Workspace.from_config()
# Create experiment
exp = Experiment(workspace=ws, name='regression-demo')
# Define training script
script_config = SKLearn(source_directory='.', 
                        script='train.py',
                        compute_target='cpu-cluster')
run = exp.submit(config=script_config)
run.wait_for_completion(show_output=True)

For a full walkthrough, see the official AzureML tutorial.