azureml-core
Azure Machine Learning core packages, modules, and classes
Decision gist · record as of 2026-08-14
Yes, if you are maintaining or extending existing Azure ML workflows already using this SDK. The package is stable, has low install friction, and carries no known vulnerabilities. However, do not start new projects with it—it is deprecated and will receive only security fixes until June 2026. Evaluate newer Azure ML SDKs or alternative ML platforms for greenfield work. Review the unclear license terms before production deployment.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.8 or later, and an Azure subscription with a configured ML workspace and authentication credentials.
- Low install friction with a pure-wheel distribution.
- Marked active with a recent release, though the package is deprecated and will receive only security fixes until June 2026.
License · maintenance · safety
(unclear) — License treatment is unclear; the raw license URL points to a Microsoft terms page rather than a standard SPDX identifier, so review the linked license before committing to production use.
last release 2026-06-16 (59 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,644,852 downloads/mo, #3,693 on PyPI
Alternatives
Verify before relying
pip install azureml-core
from azureml.core import Workspace
ws = Workspace.from_config()- Whether the June 2026 deprecation end-of-life affects your project timeline and whether a migration path to newer Azure ML SDKs is documented.
- Specific compatibility guarantees for the 34 runtime dependencies across different Azure service versions.
What it is and what it does
azureml-core is the foundational Python SDK for Azure Machine Learning, providing APIs to create and manage workspaces, submit and track training experiments, manage compute resources, and work with datasets and models. It abstracts Azure's ML infrastructure behind a Python interface, allowing you to orchestrate training jobs, log metrics, and deploy models without directly managing cloud resources.
The package is production-stable and widely used (top 5000 on PyPI), but is now deprecated with security-only maintenance through June 2026. It carries 34 runtime dependencies including Azure management libraries, authentication tools, and utilities like docker and paramiko. If you have existing code using this SDK or need to maintain legacy ML workflows on Azure, it remains functional; for new projects, you should evaluate whether to adopt a newer Azure ML SDK or alternative platform.
Use it for
- Submit and monitor training jobs on Azure compute clusters from a local Python script or notebook.
- Log metrics, models, and artifacts from experiments to track ML workflow history and reproducibility.
- Register and version datasets in Azure ML to share and reuse training data across team members.
- Deploy trained models as web services or batch endpoints on Azure infrastructure.
- Manage compute targets (VMs, clusters, Kubernetes) for distributed training and inference.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are maintaining or extending existing Azure ML workflows already using this SDK.
The package is stable, has low install friction, and carries no known vulnerabilities. However, do not start new projects with it—it is deprecated and will receive only security fixes until June 2026. Evaluate newer Azure ML SDKs or alternative ML platforms for greenfield work. Review the unclear license terms before production deployment.
Install
azureml-core on PyPI
Before you install
Low install friction with a pure-wheel distribution. Marked active with a recent release, though the package is deprecated and will receive only security fixes until June 2026.
Requires Python 3.8 or later, and an Azure subscription with a configured ML workspace and authentication credentials.
License in practice
License treatment is unclear; the raw license URL points to a Microsoft terms page rather than a standard SPDX identifier, so review the linked license before committing to production use.
Quickstart
pip install azureml-core
from azureml.core import Workspace
ws = Workspace.from_config()Verify before relying
- Whether the June 2026 deprecation end-of-life affects your project timeline and whether a migration path to newer Azure ML SDKs is documented.
- Specific compatibility guarantees for the 34 runtime dependencies across different Azure service versions.
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 | 34 packagespytzbackports.tempfilepathspecrequestsmsalmsal-extensionsknackazure-corepkginfoargcompletehumanfriendlyparamikoazure-mgmt-resourceazure-mgmt-containerregistryazure-mgmt-storageazure-mgmt-keyvaultazure-mgmt-authorizationazure-mgmt-networkazure-graphrbacazure-commonmsrestmsrestazureurllib3packagingpython-dateutilndg-httpsclientSecretStoragejsonpicklecontextlib2docker |
| Maintenance | Actively maintained 59 days since the last release |
| First released | |
| Downloads | 1,644,852 / month, #3,693 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 :: System AdministratorsProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: azureml_core-1.61.0.post4-py3-none-any.whl
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See also azureml-mlflow · azureml-train-core · model-index · openml · azureml · azureml-sdk · azureml-train-restclients-hyperdrive · azureml-featurestore · azureml-defaults · azureml-train