{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development"},{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/2"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/3"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence"}],"enrichment":{"capability":"Kubeflow Pipelines is a Python SDK for defining, deploying, and managing machine learning workflows as containerized task graphs on Kubernetes clusters.","skillfed_tags":["kubernetes","ml-orchestration","workflow-dag"],"use_cases":["Define multi-step ML training workflows with data preprocessing, model training, and evaluation as separate containerized tasks.","Schedule recurring pipeline runs on a Kubernetes cluster with different parameters or datasets.","Organize and track multiple pipeline executions as experiments with artifact storage and metadata logging.","Build reusable pipeline components that can be composed into different workflows across teams.","Deploy production ML systems where task dependencies, retries, and resource allocation are managed by Kubernetes."],"what_it_does":"Kubeflow Pipelines is a Python SDK for building machine learning workflows as directed acyclic graphs of containerized tasks. You define pipeline components using Python decorators, compose them into workflows, and submit them to a Kubeflow backend running on Kubernetes. The SDK handles task orchestration, artifact management, and provides integration with Google Cloud services through its dependencies on google-cloud-storage and google-auth.\n\nThe package is designed for teams building reproducible, scalable ML systems. It lets you parameterize pipelines, schedule recurring runs, organize executions into experiments, and visualize results through the Kubeflow Dashboard. Dependencies include Kubernetes client libraries, protocol buffers for serialization, and CLI tools via click for command-line interaction.","worth_installing":"Yes, if you have a Kubeflow backend deployed on Kubernetes and need to orchestrate multi-step ML workflows. The package is actively maintained, permissively licensed, and has low install friction. No known vulnerabilities. Install only if you already have or plan to set up a Kubeflow infrastructure; it is not a standalone ML framework."},"id":"kfp","links":{"html":"https://skillfed.io/packages/kfp","md":"https://skillfed.io/packages/kfp.md","pypi":"https://pypi.org/project/kfp/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-09","license_spdx":null,"license_treatment":"permissive","name":"kfp","python_support":"supports_current","summary":"Kubeflow Pipelines SDK"},"popularity":{"monthly_downloads":16374224,"position":1156,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"2.17.0"}