azureml-pipeline-steps
Aeva : represents a unit of computation in azureml-pipeline
Decision gist · record as of 2026-08-14
Yes, if you are building machine learning pipelines on Azure ML. It is a required component of the Azure ML SDK for pipeline orchestration, has low install friction, is actively maintained, and has no known vulnerabilities. The unclear license requires review before use in proprietary contexts, but it is standard for Microsoft Azure SDKs. Install it as part of your Azure ML workflow, not as a standalone tool.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.8 or later (supports 3.8, 3.9, 3.10, 3.11).
- Intended for use within an Azure ML workspace or SDK environment.
- Low install friction with a pure Python wheel.
License · maintenance · safety
(unclear) — License treatment is unclear—the package points to a Microsoft-hosted license URL rather than a standard SPDX identifier. Review the linked license before use in proprietary or restricted contexts.
last release 2026-02-25 (170 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 230,209 downloads/mo, #9,114 on PyPI
Alternatives
Verify before relying
pip install azureml-pipeline-steps
from azureml.pipeline.steps import PythonScriptStep
from azureml.pipeline.core import Pipeline
# Define a step and add it to a pipeline
step = PythonScriptStep(script_name='train.py')
pipeline = Pipeline(steps=[step])- Whether the Microsoft license URL resolves to a standard open-source or proprietary license agreement.
- Whether azureml-train-core and azureml-train-automl-client introduce heavy or optional transitive dependencies.
- Specific step types and APIs available in version 1.62.0 beyond the generic 'unit of computation' description.
What it is and what it does
azureml-pipeline-steps is a component of the Azure Machine Learning SDK that defines the step types used to construct pipelines—units of computation that can be orchestrated and executed on Azure ML infrastructure. It sits between azureml-pipeline-core (which manages the pipeline graph) and azureml-train-core and azureml-train-automl-client (which provide the training and AutoML execution logic). The package is production-stable, supports Python 3.8–3.11, and has been actively maintained since its 2018 release.
You install it when you need to programmatically define and run machine learning workflows on Azure—for example, to chain data preparation, model training, and evaluation steps into a reproducible pipeline. It is not a standalone tool; it is part of the larger azureml ecosystem and requires an Azure ML workspace to execute pipelines.
Use it for
- Define a multi-step training pipeline that preprocesses data, trains a model, and evaluates it on Azure ML.
- Orchestrate automated machine learning (AutoML) jobs as steps within a larger pipeline workflow.
- Build reproducible ML workflows that can be scheduled, versioned, and shared across a team.
- Chain custom Python scripts and pre-built training components into a single executable pipeline.
- Integrate data processing steps with model training in a single Azure ML pipeline definition.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building machine learning pipelines on Azure ML.
It is a required component of the Azure ML SDK for pipeline orchestration, has low install friction, is actively maintained, and has no known vulnerabilities. The unclear license requires review before use in proprietary contexts, but it is standard for Microsoft Azure SDKs. Install it as part of your Azure ML workflow, not as a standalone tool.
Install
azureml-pipeline-steps on PyPI
Before you install
Low install friction with a pure Python wheel. Maintenance status is active with a recent release (170 days ago). Depends on three azureml packages (azureml-train-core, azureml-train-automl-client, azureml-pipeline-core), which may add transitive dependencies to your environment.
Requires Python 3.8 or later (supports 3.8, 3.9, 3.10, 3.11). Intended for use within an Azure ML workspace or SDK environment.
License in practice
License treatment is unclear—the package points to a Microsoft-hosted license URL rather than a standard SPDX identifier. Review the linked license before use in proprietary or restricted contexts.
Quickstart
pip install azureml-pipeline-steps
from azureml.pipeline.steps import PythonScriptStep
from azureml.pipeline.core import Pipeline
# Define a step and add it to a pipeline
step = PythonScriptStep(script_name='train.py')
pipeline = Pipeline(steps=[step])Verify before relying
- Whether the Microsoft license URL resolves to a standard open-source or proprietary license agreement.
- Whether azureml-train-core and azureml-train-automl-client introduce heavy or optional transitive dependencies.
- Specific step types and APIs available in version 1.62.0 beyond the generic 'unit of computation' description.
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 | 3 packagesazureml-train-coreazureml-train-automl-clientazureml-pipeline-core |
| Maintenance | Actively maintained 170 days since the last release |
| First released | |
| Downloads | 230,209 / month, #9,114 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/ResearchProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: azureml_pipeline_steps-1.62.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “azure ml pipeline steps”
- azureml-pipeline-stepsDefines step types for building computation units in Azure Machine…
- azureml-pipelineBuilds, optimizes, and manages machine learning workflows in Azure by…
- azureml-fsspecProvides a filesystem interface to Azure Machine Learning datastores…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also azure-ml-component · azureml-pipeline · azureml-pipeline-core · azureml-automl-core · azureml-train-automl-client · azure-ai-ml · google-cloud-pipeline-components · sagemaker-mlops · databricks-automl-runtime · zenml