Kubeflow
UnclaimedThe open-source foundation for building and deploying AI platforms on Kubernetes.
Visit WebsiteTL;DR - Kubeflow
- An open-source platform for AI/ML on Kubernetes.
- Provides modular tools for the entire ML lifecycle.
- Enables scalable, portable, and composable AI infrastructure.
Pricing: Free forever
Best for: Individuals & startups
Pricing
Free
Kubeflow is completely free to use with no hidden costs.
About Kubeflow
Kubeflow is an open-source project that provides a collection of tools and components for building, deploying, and managing machine learning (ML) workflows on Kubernetes. It aims to make the deployment of ML systems on various infrastructures as simple, portable, and scalable as possible, by leveraging the power of Kubernetes.
Designed for AI platform teams, data scientists, and ML engineers, Kubeflow allows users to utilize individual components independently or deploy a complete AI reference platform. It offers a modular and composable architecture, backed by a rich ecosystem of Kubernetes-native projects that cover the entire AI lifecycle, from data preparation and model training to serving and monitoring. Kubeflow is battle-tested, community-built, and can be deployed anywhere Kubernetes runs, making it a flexible solution for diverse ML needs.
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Kubeflow FAQ
Kubeflow is an open-source project that provides a platform for developing, deploying, and managing machine learning (ML) workflows on Kubernetes. It offers a collection of tools and components designed to make ML operations scalable, portable, and composable.
Kubeflow is an open-source project, meaning it is free to use. However, there may be costs associated with the underlying Kubernetes infrastructure and cloud services you choose to deploy it on.
Yes, Kubeflow is completely free as it is an open-source project. Users can download, use, and contribute to the project without any licensing fees.
Kubeflow is primarily for AI platform teams, data scientists, and ML engineers who need to build, deploy, and manage machine learning models and workflows on Kubernetes in a scalable and portable manner.