Open-source platform for rapidly deploying machine learning models on Kubernetes
Go with the flow — your usual workflow
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.
Built on Kubernetes, runs on any cloud and on premises
Agnostic and independent
Framework agnostic, supports top ML libraries, toolkits and languages
Runtime inference graphs
Advanced deployments with experiments, ensembles and transformers
Platforms integrated with Seldon
The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Read the docs and explore the end-to-end machine learning demo project to learn how Seldon integrates with Kubeflow.
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. Read more about Seldon’s integration with OpenShift.
TensorFlow is an open-source software library for high performance numerical computation.
Sklearn is a common machine learning toolkit for Python, offering simple and efficient tools for data mining and data analysis.
R is a language and environment for statistical computing and graphics.
H2O is a fully open-source, distributed in-memory machine learning platform with linear scalability.