What does this skill do, and when should you use it?
This skill targets Azure Machine Learning SDK v2 for Python and helps agents manage workspaces, jobs, models, data assets, and compute. It covers authentication, client lifecycle, command jobs, asset registration, environments, datastores, and multi-step pipelines. The guidance prefers DefaultAzureCredential and deterministic cleanup through client and credential context managers. It fits Python projects already using Azure ML, but it does not replace Azure permission setup or a full infrastructure deployment workflow.
It guides the agent to install azure-ai-ml, read variables such as AZURE_SUBSCRIPTION_ID, AZURE_RESOURCE_GROUP, and AZURE_ML_WORKSPACE_NAME, create or load an MLClient, and call Azure ML SDK operations for workspaces, data assets, models, compute, jobs, pipelines, environments, and datastores. It includes examples for submitting command jobs, streaming logs, registering models, versioning assets, and configuring idle scale-down.
- A Python ML engineer managing Azure ML workspaces and listing or creating workspaces.
- A data science team registering file, folder, and versioned model assets.
- A developer submitting and monitoring Azure ML command jobs.
- An ML engineer composing preparation, training, and model-output steps into a pipeline.
- A team seeking reproducible environments, resource tags, and compute cost controls through idle scale-down.
How do you install this skill?
- Examples create or modify Azure resources; confirm the target subscription, resource group, workspace, permissions, and expected cost before execution.
- Do not rely directly on the @latest environment tag or an unpinned azure-ai-ml version; verify the current API and pin reproducible versions.
- The pipeline example leaves prep_component and train_component undefined and should not be treated as a complete runnable implementation.
- The skill does not disclose whether data leaves the local environment, how sensitive training data should be handled, or how to clean up and roll back failures.
- Mainland-China reachability and Chinese-language usage are not documented; verify network access separately when Azure services or Microsoft documentation are required.
- Shell / CLI
- Network access
- Local filesystem
Pythonazure-ai-mlazure-identity
Run npx skills add microsoft/skills in your project and select azure-ai-ml-py in the installation wizard. The skill is installed in the selected agent directory, such as .github/skills/ for GitHub Copilot. Install the SDK used by the examples with pip install azure-ai-ml. The source does not specify a Python version.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/microsoft/skills.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/.github/plugins/azure-sdk-python/skills/azure-ai-ml-py" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Give the coding agent a request containing the documented triggers, for example: Use azure-ai-ml and MLClient to create an Azure ML command job, register a dataset, and stream the job logs. The agent should follow the documented DefaultAzureCredential, environment-variable, and MLClient context-manager patterns. The source does not document a standalone command-line entry point or executable script.
What are this skill's strengths and limitations?
- Covers common Azure ML resources and an end-to-end training workflow.
- Provides explicit patterns for authentication and synchronous client lifecycle management.
- Includes examples for asset versioning, job monitoring, pipelines, environments, and compute scaling.
- MIT licensed and maintained as part of Microsoft's Agent Skills collection.
- Requires an Azure subscription, resource group, ML workspace, and suitable identity permissions.
- The skill is scoped to the azure-ai-ml Python SDK and should not be credited with sibling skills' capabilities.
- The source provides no independent test coverage or detailed failure-handling examples for this skill.
- Referenced files are named but not included, so their advanced coverage cannot be verified from the supplied material.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Azure Machine Learning Python Skill this page ✓ Microsoft · Official | 51 · Use with care | ★ 2.7k | 3mo ago | MIT |
| Azure Container Registry for Python ✓ Microsoft · Official | 53 · Use with care | ★ 2.7k | 3mo ago | MIT |
| Azure Monitor Custom Log Ingestion ✓ Microsoft · Official | 53 · Use with care | ★ 2.7k | 3mo ago | MIT |
| Azure Identity for Python ✓ Microsoft · Official | 60 · Recommended | ★ 2.7k | 3mo ago | MIT |
| NeMo Evaluator Plugin Skill ✓ NVIDIA · Official | 58 · Recommended | ★ 3.6k | 11d ago | Apache-2.0 |
How did FollowSkills review this skill?
The skill recommends DefaultAzureCredential, discourages keys, and documents production credential-chain constraints, which reduces credential exposure risk. However, create, update, streaming, and delete operations lack user confirmation, scoped permissions, data-flow disclosure, sensitive-data guidance, and rollback procedures, so points are deducted.
Installation and primary synchronous examples are broadly coherent, and the references explicitly call for current API verification. However, there is little exception handling, paging, long-running-operation failure guidance, or dependency pinning; the pipeline example also relies on undefined prep_component and train_component. The static ceiling therefore applies.
Triggers and covered Python SDK scenarios are reasonably clear, including workspaces, jobs, models, data, and compute. Inputs and outputs, non-fit boundaries, permission prerequisites, cost risks, Chinese-language support, and mainland-China network reachability are not documented, so points are deducted.
The skill has front matter, installation, authentication, lifecycle guidance, layered references, an operation table, and best practices; MIT licensing and a version field are clear. It lacks a changelog, explicit maintenance responsibility, and a concrete update path, while the latest environment tag and hidden assumptions reduce maintainability.
It covers core workspace, data, model, compute, job, pipeline, environment, and datastore tasks, and the examples provide a useful starting point. Production use still requires component definitions, error handling, cleanup paths, and dependency/version control; some output is not directly runnable, so the static score is capped at 7 and reduced accordingly.
The material contains auditable source examples, reference indexes, and repository-level CI/evaluation workflows. It does not show skill-specific tests, actual execution results, or independent corroboration for the selected skill, so only limited static evidence is credited.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →