Machine learning for Python apps on Azure

The following articles help you get started with Azure Machine Learning. Azure Machine Learning v2 REST APIs, Azure CLI extension, and Python SDK accelerate the production machine learning lifecycle. The links in this article target v2, which is recommended if you're starting a new machine learning project.

Getting started

The workspace is the top-level resource for Azure Machine Learning, providing a centralized place to work with all the artifacts you create when you use Azure Machine Learning.

• Quickstart: Get started with Azure Machine Learning
• Manage Azure Machine Learning workspaces in the portal or with the Python SDK (v2)
• Run Jupyter notebooks in your workspace
• Tutorial: Model development on a cloud workstation

Deploy models

Deploy machine learning models for real-time inference.

• Tutorial: Designer - deploy a machine learning model
• Deploy and score a machine learning model by using an online endpoint

Automated machine learning

Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time-consuming, iterative tasks of machine learning model development.

• Train a regression model with AutoML and Python (SDK v1)
• Set up AutoML training for tabular data with the Azure Machine Learning CLI and Python SDK (v2)

Data access

With Azure Machine Learning, you can bring data from a local machine or an existing cloud-based storage.

• Create and manage data assets
• Tutorial: Upload, access and explore your data in Azure Machine Learning
• Access data in a job

Machine learning pipelines

Use machine learning pipelines to create a workflow that stitches together various ML phases.

• Use Azure Pipelines with Azure Machine Learning
• Create and run machine learning pipelines using components with the Azure Machine Learning SDK v2
• Tutorial: Create production ML pipelines with Python SDK v2 in a Jupyter notebook