Azure Machine Learning: Your Gateway to AI Innovation
Azure Machine Learning is a cloud-based service that you can use to train, deploy, manage, and track machine learning models. It accelerates your AI projects with a comprehensive set of tools and resources. Whether you're a seasoned data scientist or new to machine learning, Azure ML provides the flexibility and scalability you need to succeed.
What is Azure Machine Learning?
Azure Machine Learning is a managed cloud service that helps you accelerate and manage the machine learning lifecycle. It provides an integrated environment for all stages of your ML workflow, from data preparation and model training to deployment and monitoring.
Getting Started with Azure ML
Begin your journey with Azure Machine Learning by setting up your workspace. A workspace is the top-level resource in Azure Machine Learning that provides a centralized place to work with all the artifacts you create when you use Azure Machine Learning.
- Create an Azure ML Workspace: Use the Azure portal, Azure CLI, or SDKs to create your workspace.
- Install the Azure ML SDK: Get the necessary tools to interact with your workspace programmatically.
- Explore the Designer: A visual drag-and-drop interface for building ML models without coding.
- Use Notebooks: Integrated Jupyter notebooks for code-first development.
For a step-by-step guide, check out the Beginner Tutorials.
Key Features
- Automated ML (AutoML): Automatically iterate through algorithms and hyperparameters to find the best model for your data.
- Designer: A visual drag-and-drop interface for building models.
- Notebooks: Integrated Jupyter notebooks for code-first ML development.
- Responsible AI: Tools for fairness, interpretability, and privacy.
- MLOps: Capabilities for managing the end-to-end machine learning lifecycle, including CI/CD pipelines.
- Scalable Compute: Access to powerful compute resources like CPU and GPU clusters.
- Data Management: Tools for preparing, versioning, and registering your datasets.
Tutorials and Learning Resources
Dive deeper with our comprehensive tutorials:
- Create your first Azure ML workspace
- Train an image classification model with AutoML
- Build a regression model using the Designer
- Deploy a real-time ML service
- Introduction to MLOps with Azure ML
Azure ML SDKs
Interact with Azure Machine Learning using powerful SDKs available for Python and R.
Python SDK Example:
1from azure.ai.ml import MLClient
2from azure.identity import DefaultAzureCredential
3
4# Authenticate and create MLClient
5credential = DefaultAzureAzureCredential()
6ml_client = MLClient(
7 credential=credential,
8 subscription_id="YOUR_SUBSCRIPTION_ID",
9 resource_group_name="YOUR_RESOURCE_GROUP",
10 workspace_name="YOUR_WORKSPACE_NAME"
11)
12
13# List all experiments
14for exp in ml_client.experiments.list():
15 print(f"Experiment: {exp.name}")
Learn more about the Python SDK documentation and R SDK documentation.
Azure ML CLI Extension
Manage your Azure ML resources efficiently using the Azure CLI extension.
CLI Example: Create a compute instance
1az ml compute create --name "cpu-cluster" --type "amlcompute" --min-instances 0 --max-instances 4 --size "STANDARD_DS3_V2"
Find more commands in the Azure CLI ML extension reference.
Code Examples and Samples
Explore a wide range of practical examples demonstrating various Azure ML capabilities: