Kubeflow Pipelines
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Explore more → **Kubeflow Pipelines**
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.
What other technologies are related to Kubeflow Pipelines?
Kubeflow Pipelines Competitor Technologies
ML Run
No summary available
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.
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.
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.
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.
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.
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Kubeflow Pipelines Complementary Technologies
Kubeflow KF Serving
No summary available
KFServing (now superseded by KServe) is a component of Kubeflow that focuses on model serving.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.
TF Serving
No summary available
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.
XGBoost
XGBoost is a popular machine learning algorithm. Kubeflow Pipelines can be used to train XGBoost models. Hence, it's complementary.
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Which job functions commonly mention Kubeflow Pipelines?
MLOps Engineer
0.4% of all MLOps Engineer jobs mention Kubeflow Pipelines
View 30
jobs on Sumble
Applied Scientist
0.1% of all Applied Scientist jobs mention Kubeflow Pipelines
View 12
jobs on Sumble
Machine Learning
0.1% of all Machine Learning jobs mention Kubeflow Pipelines
View 86
jobs on Sumble
Data Scientist
0.1% of all Data Scientist jobs mention Kubeflow Pipelines
View 129
jobs on Sumble
Platform Engineer
20 Platform Engineer jobs mention Kubeflow Pipelines
View 20
jobs on Sumble
Which organizations are mentioning Kubeflow Pipelines?
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