---
id: azureml-core
version: "1.61.0.post4"
license: https://aka.ms/azureml-sdk-license
license_treatment: unclear
maintenance: active
---

# azureml-core — Azure Machine Learning core packages, modules, and classes

License: unclear · Maintenance: active · Downloads: 1.6M/mo

## 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 above — verify before relying.

Provides core APIs and utilities for managing Azure Machine Learning workspaces, experiments, compute resources, datasets, models, and training runs.

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

pip install azureml-core
uv add azureml-core
poetry add azureml-core

## Installing azureml-core

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.

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()

Requires Python 3.8 or later, and an Azure subscription with a configured ML workspace and authentication credentials.

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: https://aka.ms/azureml-sdk-license (unclear)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 1.6M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

azure machine learning workspace management, azure ml experiment tracking, azure ml compute targets, azure ml model registry, azure ml dataset handling, machine learning on azure, azure ml training runs, azure ml web services, azure-cloud, ml-infrastructure, deprecated

[View on SkillFed](https://skillfed.io/packages/azureml-core) · [View on PyPI](https://pypi.org/project/azureml-core/)