Core

Open-source platform for rapidly deploying machine learning models on Kubernetes

Core

Open-source platform for rapidly deploying machine learning models on Kubernetes

Deploy Machine Learning Models At Scale - Fast

Seldon Core, our open-source framework, makes it easier and faster to deploy your machine learning models and experiments at scale on Kubernetes.

Seldon Core serves models built in any open-source or commercial model building framework. You can make use of powerful Kubernetes features like custom resource definitions to manage model graphs. And then connect your continuous integration and deployment (CI/CD) tools to scale and update your deployment.

Runs anywhere

Runs anywhere

Built on Kubernetes, runs on any cloud and on premises

Agnostic and independent

Agnostic and independent

Framework agnostic, supports top ML libraries, toolkits and languages

Runtime inference graphs

Runtime inference graphs

Advanced deployments with experiments, ensembles and transformers

Seldon Core stack

Our open-source framework makes it easier and faster to deploy your machine learning models and experiments at scale on Kubernetes.

Platform Integrations

Kubeflow
The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. 

RedHat: OpenShift
OpenShift combines application lifecycle management – including image builds, continuous integration, deployments, and updates – with Kubernetes. Use OpenShift as a managed service, in the cloud, or in your own datacenter. 

Supported Toolkits

Get Started

You’ll find Seldon Core on GitHub 

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