azure-ai-ml
Microsoft Azure Machine Learning Client Library for Python
Decision gist · record as of 2026-08-14
Yes. The package is production-stable (v2 GA), actively maintained, has no known vulnerabilities, and low install friction. Install it if you need to programmatically interact with Azure Machine Learning services—it is the official and recommended SDK for that purpose. Verify your Azure subscription and workspace setup before use.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires an active Azure subscription, an Azure Machine Learning Workspace, and valid Azure credentials configured in your environment.
- Low install friction with a pure-wheel distribution.
- Actively maintained with a release 31 days ago and 5588 repository stars.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions, making it suitable for most production environments.
last release 2026-07-14 (31 days) · last repo commit 2026-08-14 · 5,588 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 4,141,760 downloads/mo, #2,371 on PyPI
Alternatives
Verify before relying
pip install azure-ai-ml
from azure.ai.ml import MLClient
ml_client = MLClient(
credential,
subscription_id="your-subscription-id",
resource_group_name="your-resource-group",
workspace_name="your-workspace"
)
job = ml_client.jobs.get("job-name")- Whether telemetry collection in Jupyter notebooks can be fully disabled or only opted out at client initialization
- Performance characteristics when managing large numbers of jobs or components in a single pipeline
- Whether the package supports disconnected or air-gapped Azure environments
- What authentication mechanisms are supported beyond the example shown in the description
What it is and what it does
Azure ML is the official Python SDK v2 for Azure Machine Learning, providing a unified client interface (MLClient) to create, manage, and execute machine learning workloads on Azure. It abstracts Azure ML resources—jobs, models, components, compute clusters, datastores—into Python objects that can be created programmatically or loaded from YAML, validated against schemas, and submitted to the service via authenticated REST calls.
The SDK supports standalone jobs (command, sweep), reusable pipeline components, AutoML for classification/regression/forecasting/vision/NLP tasks, managed online and batch inference endpoints, and resource lifecycle management. It depends on azure-core, azure-mgmt-core, azure-storage-blob, and related Azure libraries for authentication, HTTP communication, and data access. Telemetry is collected in Jupyter environments by default but can be disabled.
Use it for
- Submit and monitor standalone training jobs (Python, R, shell scripts) to Azure compute without local execution
- Build reusable ML pipeline components and orchestrate multi-step workflows with parameter sweeps
- Run AutoML experiments for tabular classification, regression, time-series forecasting, or vision/NLP tasks
- Deploy trained models to managed online endpoints for real-time inference or batch endpoints for bulk scoring
- Manage Azure ML workspace resources (compute clusters, datastores, environments) programmatically
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is production-stable (v2 GA), actively maintained, has no known vulnerabilities, and low install friction. Install it if you need to programmatically interact with Azure Machine Learning services—it is the official and recommended SDK for that purpose. Verify your Azure subscription and workspace setup before use.
Install
azure-ai-ml on PyPI
Before you install
Low install friction with a pure-wheel distribution. Actively maintained with a release 31 days ago and 5588 repository stars. Supports Python 3.9 through 3.14 and depends on 16 runtime packages, mostly within the Azure SDK ecosystem.
Requires an active Azure subscription, an Azure Machine Learning Workspace, and valid Azure credentials configured in your environment.
License in practice
MIT license permits commercial and private use with minimal restrictions, making it suitable for most production environments.
Quickstart
pip install azure-ai-ml
from azure.ai.ml import MLClient
ml_client = MLClient(
credential,
subscription_id="your-subscription-id",
resource_group_name="your-resource-group",
workspace_name="your-workspace"
)
job = ml_client.jobs.get("job-name")Verify before relying
- Whether telemetry collection in Jupyter notebooks can be fully disabled or only opted out at client initialization
- Performance characteristics when managing large numbers of jobs or components in a single pipeline
- Whether the package supports disconnected or air-gapped Azure environments
- What authentication mechanisms are supported beyond the example shown in the description
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 16 packagespyyamlazure-coreazure-mgmt-coremarshmallowjsonschematqdmstrictyamlcoloramapyjwtazure-storage-blobazure-storage-file-shareazure-storage-file-datalakepydashisodatetyping-extensionsazure-monitor-opentelemetry |
| Maintenance | Actively maintained 31 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 4,141,760 / month, #2,371 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.9 |
Evidence: azure_ai_ml-1.34.1-py3-none-any.whl
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