skills.rest
microsoft
microsoftOFFICIAL
Explore all skills by this author →
Website𝕏@OpenAtMicrosoftGitHub

azure-ai-ml-py

Manage Azure ML resources programmatically with MLClient and DefaultAzureCredential.

2.9k|323|Updated Jan 16, 2026
Search Tags:#python#data#workspace#pipelines#compute#azure-ml#mlclient
One-click install
npx skills add https://github.com/microsoft/skills --skill azure-ai-ml-py-microsoft
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: azure-ai-ml-py
Source: https://github.com/microsoft/skills/tree/main/.github/plugins/azure-sdk-python/skills/azure-ai-ml-py
Command: npx skills add https://github.com/microsoft/skills --skill azure-ai-ml-py-microsoft

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Simplify and automate the management of Azure Machine Learning resources (workspaces, datasets, models, compute, and jobs) from Python.

Core Features & Use Cases

  • Workspace management: create, list, update, and delete ML workspaces.
  • Data and model assets: register, version, and manage datasets and models in the registry.
  • Compute and environments: configure compute targets and reproducible environments for runs.
  • Jobs and pipelines: define and run training or inference jobs, and compose pipelines for end-to-end workflows.
  • Use Case: a data scientist scripts a workflow to provision a workspace, register data, train a model, and deploy it to production.

Quick Start

Install the azure-ai-ml package and create an MLClient using DefaultAzureCredential to begin managing AML resources.

Frequently Asked Questions about azure-ai-ml-py

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I manage Azure ML workspaces and resources programmatically in Python?▼

You can manage Azure ML workspaces by creating an MLClient using DefaultAzureCredential to programmatically create, list, update, and delete resources like datasets, models, and compute.

Can I script end-to-end machine learning pipelines using the Azure ML Python SDK?▼

Yes, you can use the Azure ML Python SDK to define and run training or inference jobs, and compose pipelines that orchestrate end-to-end machine learning workflows across your environments.

How do I register and version datasets and models in Azure ML?▼

To register and version datasets and models in Azure ML, use the MLClient to programmatically manage data assets and models within the Azure ML registry for reproducible runs.

What's the best way to configure compute targets and reproducible environments for Azure ML jobs?▼

The best way to configure compute targets and reproducible environments is using the Python SDK to script compute resource provisioning and environment definitions before running your Azure ML jobs.

Do I need DefaultAzureCredential to authenticate MLClient for Azure ML operations?▼

Yes, creating an MLClient to begin managing Azure ML resources requires using DefaultAzureCredential or config-based access to authenticate your Python scripts for secure automated workflows.

Can I automate the deployment of trained models to production using Azure ML?▼

You can automate model deployment by scripting a workflow with MLClient that provisions a workspace, registers data, trains a model, and deploys it to production within automated contexts.

Related Skills

Explore More Software Engineering Skills→
davidrrowleydavidrrowley

azure-ai-ml-py

Manage Azure Machine Learning resources and workflows with the Azure ML SDK v2.

Community
Intermediate
randoneeringrandoneering

spcs-inference

Deploy registered Snowflake models to Snowpark Container Services for real-time inference.

Community
Advanced
AshleyHollisAshleyHollis

azure-cli-operations

Manage Azure resources via Azure CLI for ACR, AKS, Storage, and Key Vault.

Community
Intermediate
nelohenriqnelohenriq
1

lambda-labs-gpu-cloud

Provision and manage on-demand GPU compute for ML workloads via the Lambda Cloud API.

Community
Advanced