azureml-mlflow
Contains the integration code of AzureML with Mlflow.
Decision gist · record as of 2026-08-14
Yes, if you use Azure Machine Learning and want to standardize on MLflow's tracking interface. The package is actively maintained, has no known vulnerabilities, and low install friction. It's the official integration layer, so it's the right choice for AzureML+MLflow workflows. Skip it if you're not using AzureML or prefer MLflow's native server setup.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires an active Azure Machine Learning workspace and valid credentials configured via Workspace.from_config().
- Low install friction with a pure-Python wheel.
- Actively maintained with a release within the last 44 days.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions; suitable for most projects without licensing concerns.
last release 2026-07-01 (44 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,731,364 downloads/mo, #2,916 on PyPI
Alternatives
Verify before relying
pip install azureml-mlflow
import mlflow
from azureml.core import Workspace
workspace = Workspace.from_config()
mlflow.set_tracking_uri(workspace.get_mlflow_tracking_uri())- Specific MLflow version compatibility constraints beyond what mlflow-skinny provides
- Performance characteristics when logging large artifacts or high-frequency metrics
- Whether all MLflow tracking features are fully supported or if some are limited in Azure context
What it is and what it does
azureml-mlflow is a bridge between MLflow and Azure Machine Learning that lets you use MLflow's standard experiment tracking APIs while storing results in an AzureML workspace. It wraps the connection setup so that when you call mlflow.set_tracking_uri() with an AzureML workspace URI, your logged metrics, parameters, and artifacts flow directly into AzureML's tracking backend instead of a local or remote MLflow server.
The package depends on Azure SDK libraries (azure-identity, azure-storage-blob, azure-core) to authenticate and communicate with Azure, plus mlflow-skinny for the core tracking interface. It's designed for teams already using AzureML who want to standardize on MLflow's API without managing a separate MLflow server, or for researchers migrating experiments from local MLflow to cloud-hosted AzureML infrastructure.
Use it for
- Log experiment metrics and models from a local training script directly to an AzureML workspace using standard MLflow APIs.
- Migrate existing MLflow experiment tracking code to AzureML by changing only the tracking URI configuration.
- Centralize ML experiment tracking across a team by routing all MLflow logs through a shared AzureML workspace.
- Track hyperparameter tuning runs in AzureML while keeping your training code independent of Azure-specific APIs.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you use Azure Machine Learning and want to standardize on MLflow's tracking interface.
The package is actively maintained, has no known vulnerabilities, and low install friction. It's the official integration layer, so it's the right choice for AzureML+MLflow workflows. Skip it if you're not using AzureML or prefer MLflow's native server setup.
Install
azureml-mlflow on PyPI
Before you install
Low install friction with a pure-Python wheel. Actively maintained with a release within the last 44 days. Requires 11 runtime dependencies including Azure SDK components and MLflow, which are standard for Azure ML workflows.
Requires an active Azure Machine Learning workspace and valid credentials configured via Workspace.from_config().
License in practice
MIT license permits commercial and private use with minimal restrictions; suitable for most projects without licensing concerns.
Quickstart
pip install azureml-mlflow
import mlflow
from azureml.core import Workspace
workspace = Workspace.from_config()
mlflow.set_tracking_uri(workspace.get_mlflow_tracking_uri())Verify before relying
- Specific MLflow version compatibility constraints beyond what mlflow-skinny provides
- Performance characteristics when logging large artifacts or high-frequency metrics
- Whether all MLflow tracking features are fully supported or if some are limited in Azure context
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4.0,>=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 11 packagesjsonpicklemlflow-skinnyazure-identitymsrestazure-coreazure-mgmt-coreazure-storage-blobazure-commoncryptographypython-dateutilpytz |
| Maintenance | Actively maintained 44 days since the last release |
| First released | |
| Downloads | 2,731,364 / month, #2,916 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: azureml_mlflow-1.62.0.post5-py3-none-any.whl
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See also azureml · azureml-ai-monitoring · azureml-core · azureml-telemetry · comet-ml · traceml · mlflow · aim · azureml-inference-server-http · mlflow-skinny