azure-ai-ml-py
Azure ML workspace and job management
Developer Setup
Setup & Installation
npx skills add https://github.com/microsoft/skills --skill azure-ai-ml-pynpx skills add https://github.com/microsoft/skills --skill azure-ai-ml-pyOverview
What This Skill Does
Azure Machine Learning SDK v2 for Python (azure-ai-ml) lets you manage ML resources on Azure programmatically. It covers workspaces, training jobs, model registration, compute clusters, datasets, pipelines, and environments through the MLClient interface.
Application
When to use this Skill
- Configuring integration settings for custom agent workflows.
- Optimizing query execution and response latency in production.
- Developing clean, standard-compliant implementations for enterprise services.
- Troubleshooting connection timeouts and authentication handshakes.
- Monitoring API rate limits and execution pipelines programmatically.
Documentation
Show Skills.md file
Azure Machine Learning SDK v2 for Python
Client library for managing Azure ML resources: workspaces, jobs, models, data, and compute.
Installation
pip install azure-ai-ml
Environment Variables
AZURE_SUBSCRIPTION_ID=<your-subscription-id> # Required for all auth methods
AZURE_RESOURCE_GROUP=<your-resource-group> # Required for all auth methods
AZURE_ML_WORKSPACE_NAME=<your-workspace-name> # Required for all auth methods
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production
Authentication & Lifecycle
🔑 Two rules apply to every code sample below:
- Prefer
DefaultAzureCredential. It works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account/API keys — they bypass Entra audit and rotation.
- Local dev:
DefaultAzureCredentialworks as-is.
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