Welcome to Azure Machine Learning
Azure Machine Learning is a cloud-based environment for training, deploying, automating, managing, and tracking machine learning models. It's designed to accelerate your machine learning lifecycle with a comprehensive set of capabilities.
Key Features
Accelerated Development
Build and train models faster with managed compute, integrated notebooks, and automated ML capabilities.
Learn MoreExperiment Tracking
Log experiments, track metrics, and compare runs to understand model performance and reproducibility.
Learn MoreScalable Compute
Leverage a wide range of compute options, from CPUs to GPUs, for training and inference, both in the cloud and on-premises.
Learn MoreModel Management
Register, version, and manage your trained models efficiently, preparing them for deployment.
Learn MoreDeployment Options
Deploy your models as real-time web services or batch inference pipelines to various targets.
Learn MoreMLOps & Automation
Automate your ML workflows with pipelines and CI/CD integration for seamless model lifecycle management.
Learn MoreGetting Started
Ready to dive in? Here are some common starting points:
- Create an Azure Machine Learning Workspace
- Build a classification model with AutoML
- Train a model on local compute
Code Example
Here's a basic example of how you might interact with Azure ML using Python SDK:
from azureml.core import Workspace
# Load workspace
ws = Workspace.from_config()
print(f"Workspace name: {ws.name}")
print(f"Subscription ID: {ws.subscription_id}")
print(f"Resource group: {ws.resource_group}")
Explore the left-hand navigation for more detailed documentation on specific services and features.