Seldon Core

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.

Runs anywhere

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

Seldon Core stack

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

Read the install guide

Platforms integrated with Seldon

Kubeflow
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.

IBM FfDL
Leveraging the power of Kubernetes, FfDL provides a scalable, resilient, and fault-tolerant deep-learning framework. Run the FfDL demo project to see how Seldon integrates with IBM FfDL.

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. Read more about Seldon’s integration with OpenShift.

Supported toolkits

TensorFlow
TensorFlow is an open-source software library for high performance numerical computation.

scikit-learn
Sklearn is a common machine learning toolkit for Python, offering simple and efficient tools for data mining and data analysis.

Spark
MLlib is Apache Spark’s scalable machine learning library. MLlib fits into Spark’s APIs and interoperates with NumPy in Python and R libraries.

R
R is a language and environment for statistical computing and graphics.

H2O
H2O is a fully open-source, distributed in-memory machine learning platform with linear scalability.

Java
Java is a general-purpose programming language used by popular frameworks like DL4J.

Marketplaces

Google Cloud Platform
We’ve introduced a commercial Kubernetes application for all users of the Google Cloud Platform Marketplace.
Launch Now

Amazon Web Services
AWS customers can now easily deploy machine learning models and experiments at scale with Seldon Core.
Launch Now

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