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azure-ai-ml-py

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

Updated Apr 12, 2026
Search Tags:#pipelines#compute#model-registry#data-assets#azure-ml#azure-ai-ml#mlclient
One-click install
npx skills add https://github.com/davidrrowley/CortexYouV3 --skill azure-ai-ml-py-davidrrowley
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: azure-ai-ml-py
Source: https://github.com/davidrrowley/CortexYouV3/tree/main/.agents/skills/azure-ai-ml-py
Command: npx skills add https://github.com/davidrrowley/CortexYouV3 --skill azure-ai-ml-py-davidrrowley

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a concise, Python-first interface for provisioning and managing Azure Machine Learning resources, removing manual Azure portal operations and repetitive SDK usage across workspaces, compute, data, models, and pipelines.

Core Features & Use Cases

  • Workspace & Compute Management: Create, list, and configure workspaces and compute clusters with recommended async handling for long-running operations.
  • Data and Model Registry: Register and version data assets and models, list assets, and retrieve specific versions for reproducible ML workflows.
  • Jobs & Pipelines: Submit command jobs, stream logs, and compose multi-step pipelines using the Azure ML SDK v2 DSL, suitable for training, evaluation, and deployment automation.
  • Use Case: A data scientist can register a dataset, create a compute cluster, run a training job, and register the resulting model programmatically as part of CI/CD for ML.

Quick Start

Use the azure-ai-ml-py skill to create a workspace, register a dataset, and submit a training job using your AZURE_SUBSCRIPTION_ID, AZURE_RESOURCE_GROUP, and AZURE_ML_WORKSPACE_NAME environment variables.

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 compute clusters using Python?▼

You can manage Azure ML workspaces and compute clusters programmatically using the Azure ML SDK v2. This Skill provisions and configures resources by applying the azure-ai-ml package with Python, removing manual Azure portal operations.

What is the best way to submit Azure ML training jobs and stream logs in Python?▼

Submitting Azure ML training jobs and streaming logs is handled by the Azure ML SDK v2. You submit command jobs and stream logs using the azure-ai-ml package, handling long-running operations asynchronously with begin_*.result() patterns.

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

Registering and versioning data assets and models in Azure ML is done through the Azure ML SDK v2. You use the azure-ai-ml package to register datasets and retrieve specific versions, ensuring reproducible ML workflows.

Do I need Azure credentials to use the Azure ML SDK v2 for pipelines?▼

Yes, Azure credentials are required to use the Azure ML SDK v2 for pipelines. You must supply Azure credentials via environment variables or DefaultAzureCredential to authenticate the azure-ai-ml package with your workspace.

Why does my Azure ML compute cluster creation hang in Python?▼

Azure ML compute cluster creation hangs in Python if long-running operations are not handled correctly. You must manage these asynchronous operations using the recommended begin_*.result() patterns in the azure-ai-ml package.

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