Tech Insights

Kubeflow Pipelines

Last updated May 19, 2025, generated by Sumble

What is Kubeflow Pipelines?

Kubeflow Pipelines is a platform for building and deploying portable, scalable machine learning (ML) workflows. It allows users to define ML pipelines using a domain-specific language (DSL), and then execute these pipelines on Kubernetes. Kubeflow Pipelines manages the execution, tracking, and versioning of each step in the workflow, enabling reproducibility and collaboration. It is commonly used for tasks such as data preprocessing, model training, and model deployment.

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What other technologies are related to Kubeflow Pipelines?

Kubeflow Pipelines Competitor Technologies

ML Run

MLRun is an open-source MLOps platform that automates and accelerates machine learning pipelines, similar to Kubeflow Pipelines. It provides features for data preparation, model training, and deployment, making it a potential alternative.

mentioned alongside Kubeflow Pipelines in 97% (57) of relevant job posts

Seldon Core

Seldon Core is an open-source platform for deploying machine learning models on Kubernetes. It offers features like advanced deployment strategies (e.g., canary deployments), monitoring, and explainability, making it a competitor to Kubeflow Pipelines, particularly in the model deployment phase.

mentioned alongside Kubeflow Pipelines in 16% (60) of relevant job posts

GCP Vertex AI

Vertex AI is Google Cloud's managed machine learning platform. It provides tools for the entire ML lifecycle, from data preparation to model deployment, overlapping with the functionality of Kubeflow Pipelines and making it a competitor, especially for users already invested in the Google Cloud ecosystem.

mentioned alongside Kubeflow Pipelines in 1% (101) of relevant job posts

MLFlow

MLflow is an open-source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a model registry. While it can be integrated with Kubeflow, its model registry and experiment tracking features overlap with some of Kubeflow's capabilities, making it a competitor.

mentioned alongside Kubeflow Pipelines in 0% (90) of relevant job posts

Airflow

Apache Airflow is a workflow management platform. While Airflow can be used to orchestrate machine learning workflows, Kubeflow Pipelines is specifically designed for ML pipelines and offers features like lineage tracking and component reusability that are more tailored to ML workflows, making them competitive.

mentioned alongside Kubeflow Pipelines in 0% (150) of relevant job posts

Kubeflow Pipelines Complementary Technologies

Kubeflow KF Serving

KFServing (now superseded by KServe) is a component of Kubeflow that focuses on model serving. Kubeflow Pipelines can be used to build and deploy models, and KFServing can then serve those models, making them complementary.

mentioned alongside Kubeflow Pipelines in 100% (51) of relevant job posts

TF Serving

TensorFlow Serving is a flexible, high-performance serving system for machine learning models. Kubeflow Pipelines can train TensorFlow models, and TF Serving can be used to deploy them. Thus, it is complementary.

mentioned alongside Kubeflow Pipelines in 31% (51) of relevant job posts

XGBoost

XGBoost is a popular machine learning algorithm. Kubeflow Pipelines can be used to train XGBoost models. Hence, it's complementary.

mentioned alongside Kubeflow Pipelines in 1% (67) of relevant job posts