azureml-sdk
Used to build and run machine learning workflows upon the Azure Machine Learning service.
Decision gist · record as of 2026-08-14
Yes, if you are committed to the Azure ecosystem and need a managed ML platform with integrated experiment tracking, AutoML, and pipeline orchestration. The low install friction and active maintenance are positive signals. However, clarify the custom license terms first, and verify that the five runtime dependencies align with your use case—this is a heavyweight, cloud-coupled package, not a lightweight local ML library.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.8 or later (up to 3.11).
- Azure credentials and a configured workspace are needed for actual service interaction.
- Low install friction with a pure-wheel distribution.
License · maintenance · safety
(unclear) — License treatment is unclear—the package references a custom license URL (https://aka.ms/azureml-sdk-license) rather than a standard SPDX identifier. Review the linked license terms before committing to production use.
last release 2026-02-25 (170 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 355,251 downloads/mo, #7,292 on PyPI
Alternatives
Verify before relying
pip install azureml-sdk
from azureml.core import Workspace
ws = Workspace.from_config()- Specific data residency and privacy implications of free-form text storage in the United States beyond what the excerpt states.
- Whether the five runtime dependencies (azureml-core, azureml-dataset-runtime, azureml-train-core, azureml-train-automl-client, azureml-pipeline) are all required or can be installed selectively.
What it is and what it does
The Azure Machine Learning SDK for Python is the official client library for building and managing machine learning workflows on Microsoft's Azure ML service. It allows data scientists and AI developers to train, evaluate, and deploy models directly from Python, with support for classical ML, AutoML, and deep learning across multiple domains. The SDK works in any Python environment—Jupyter Notebooks, local IDEs, or cloud compute—and abstracts away much of the infrastructure management by delegating to the Azure service backend.
The package depends on five azureml-* runtime components that handle core functionality (workspace management, dataset handling, training orchestration, AutoML clients, and pipeline execution). It supports Python 3.8 through 3.11 and runs on macOS, Windows, and Linux. The service stores customer-provided metadata (workspace names, experiment parameters, file metadata) in the United States, which is a data residency consideration for regulated workloads.
Use it for
- Train and register ML models using Azure's managed compute, then deploy them as REST endpoints for inference.
- Build automated machine learning (AutoML) pipelines that automatically select and tune algorithms for classification or regression tasks.
- Orchestrate multi-step training workflows using Azure ML Pipelines, including data preparation, model training, and evaluation.
- Track experiments, metrics, and model versions in a centralized Azure ML workspace for reproducibility and collaboration.
- Deploy deep learning models trained locally or on Azure compute to production endpoints with built-in monitoring.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are committed to the Azure ecosystem and need a managed ML platform with integrated experiment tracking, AutoML, and pipeline orchestration.
The low install friction and active maintenance are positive signals. However, clarify the custom license terms first, and verify that the five runtime dependencies align with your use case—this is a heavyweight, cloud-coupled package, not a lightweight local ML library.
Install
azureml-sdk on PyPI
Before you install
Low install friction with a pure-wheel distribution. Active maintenance status with a recent release (170 days ago). Depends on five azureml-* runtime packages, all part of the same ecosystem.
Requires Python 3.8 or later (up to 3.11). Azure credentials and a configured workspace are needed for actual service interaction.
License in practice
License treatment is unclear—the package references a custom license URL (https://aka.ms/azureml-sdk-license) rather than a standard SPDX identifier. Review the linked license terms before committing to production use.
Quickstart
pip install azureml-sdk
from azureml.core import Workspace
ws = Workspace.from_config()Verify before relying
- Specific data residency and privacy implications of free-form text storage in the United States beyond what the excerpt states.
- Whether the five runtime dependencies (azureml-core, azureml-dataset-runtime, azureml-train-core, azureml-train-automl-client, azureml-pipeline) are all required or can be installed selectively.
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release <4.0,>=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesazureml-coreazureml-dataset-runtimeazureml-train-coreazureml-train-automl-clientazureml-pipeline |
| Maintenance | Actively maintained 170 days since the last release |
| First released | |
| Downloads | 355,251 / month, #7,292 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/StableIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: Other/Proprietary LicenseOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: azureml_sdk-1.62.0-py3-none-any.whl
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See also azureml-train-core · azure-ai-ml · azureml-core · azureml-automl-core · azureml-train-automl · azureml-pipeline · databricks-automl-runtime · supervisely · pycaret · dotnetcore2