---
id: azureml-pipeline
version: "1.62.0"
license: https://aka.ms/azureml-sdk-license
license_treatment: unclear
maintenance: active
---

# azureml-pipeline — Used to build, optimize, and manage their machine learning workflows.

License: unclear · Maintenance: active · Downloads: 217.2K/mo

## What it is and what it does

azureml-pipeline is a Python SDK component for defining and managing machine learning workflows within Azure Machine Learning. It allows data scientists to compose reusable steps—such as data preparation, model training, hyperparameter tuning, and deployment—into executable pipelines that can be submitted to Azure for distributed execution and tracked over time.

The package depends on azureml-pipeline-core and azureml-pipeline-steps to provide the underlying pipeline infrastructure and step types. It supports Python 3.8 through 3.11 on Windows, macOS, and Linux, and is classified as production-stable. The package is part of the broader Azure ML SDK ecosystem and is intended for developers and researchers building ML workflows in Azure.

Use it for:

- Orchestrate multi-step data preparation and model training workflows that run on Azure compute resources.
- Define reusable pipeline steps and modules to standardize ML processes across teams.
- Submit and monitor long-running ML jobs without managing infrastructure directly.
- Automate hyperparameter tuning and model validation as part of a repeatable pipeline.
- Build end-to-end ML workflows from raw data ingestion through model deployment.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Builds, optimizes, and manages machine learning workflows in Azure by composing data preparation, model training, and deployment steps into executable pipelines that can be submitted and tracked.

Yes, if you are building ML workflows in Azure. The package is production-stable, actively maintained, has low install friction, and addresses a core use case for Azure ML users. The proprietary license requires verification of terms, but poses no blocker for typical development use within Azure. No known security vulnerabilities.

## Install

pip install azureml-pipeline
uv add azureml-pipeline
poetry add azureml-pipeline

## Installing azureml-pipeline

Before you install: Low install friction with a pure-Python wheel and only two runtime dependencies. Actively maintained as of 170 days ago with production-stable status.

License in practice: Licensed under a proprietary Microsoft license (https://aka.ms/azureml-sdk-license) rather than an open-source SPDX license, which may restrict commercial or derivative use depending on the terms.

Quickstart:

pip install azureml-pipeline

from azureml.pipeline.core import Pipeline
from azureml.pipeline.steps import PythonScriptStep

# Create pipeline steps and compose into a Pipeline object
pipeline = Pipeline(workspace=ws, steps=[step1, step2])

Requires an Azure Machine Learning workspace and authentication credentials to submit and track pipeline runs.

Verify before relying:

- Exact scope of the proprietary license terms and whether it permits commercial use or redistribution.
- Whether azureml-pipeline-core and azureml-pipeline-steps are independently versioned or locked to this release.

## Package facts

- License: https://aka.ms/azureml-sdk-license (unclear)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 217.2K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

azure machine learning pipelines, ml workflow orchestration, data preparation and model training, azure ml pipeline steps, machine learning pipeline management, azure-ml, workflow-orchestration, ml-pipelines

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