{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/7"}],"enrichment":{"capability":"Defines step types for building computation units in Azure Machine Learning pipelines, enabling orchestration of training, data processing, and automated ML workflows.","skillfed_tags":["azure-ml","pipeline-orchestration","ml-workflow"],"use_cases":["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."],"what_it_does":"azureml-pipeline-steps is a component of the Azure Machine Learning SDK that defines the step types used to construct pipelines\u2014units 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\u20133.11, and has been actively maintained since its 2018 release.\n\nYou install it when you need to programmatically define and run machine learning workflows on Azure\u2014for 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.","worth_installing":"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."},"id":"azureml-pipeline-steps","links":{"html":"https://skillfed.io/packages/azureml-pipeline-steps","md":"https://skillfed.io/packages/azureml-pipeline-steps.md","pypi":"https://pypi.org/project/azureml-pipeline-steps/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-02-25","license_spdx":null,"license_treatment":"unclear","name":"azureml-pipeline-steps","python_support":"supports_current","summary":"Aeva : represents a unit of computation in azureml-pipeline"},"popularity":{"monthly_downloads":230209,"position":9114,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.62.0"}