Machine learning for Python apps on Azure

The following articles help you get started with Azure Machine Learning. Azure Machine Learning's 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.

• Manage Azure Machine Learning workspaces in the portal or with the Python SDK
• Quickstart: Run Jupyter notebooks in studio and Python
• Tutorial: Azure Machine Learning in a day with the Python SDK

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 with the Azure Machine Learning Python SDK v2

Data access

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

• Create data sets
• Access datasets with Python using the Azure Machine Learning Python client library

Machine learning pipelines

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

• 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
• Deploy a data pipeline with Azure DevOps