Seldon Server

Machine Learning Platform and Recommendation Engine built on Kubernetes
Alternatives To Seldon Server
Project NameStarsDownloadsRepos Using ThisPackages Using ThisMost Recent CommitTotal ReleasesLatest ReleaseOpen IssuesLicenseLanguage
Netdata66,192
10 hours ago362gpl-3.0C
Monitor your servers, containers, and applications, in high-resolution and in real-time!
Jina19,4091612 hours ago2,453December 01, 202318apache-2.0Python
☁️ Build multimodal AI applications with cloud-native stack
Recommenders16,7812a day ago11April 01, 2022168mitPython
Best Practices on Recommendation Systems
Awesome Kubernetes14,349
a month ago15otherShell
A curated list for awesome kubernetes sources :ship::tada:
Argo Workflows13,774245114 hours ago449November 27, 2023973apache-2.0Go
Workflow Engine for Kubernetes
Kubeflow13,214311 hours ago64November 01, 2023372apache-2.0TypeScript
Machine Learning Toolkit for Kubernetes
Computervision Recipes9,182
2 months ago66mitJupyter Notebook
Best Practices, code samples, and documentation for Computer Vision.
Metaflow7,19712510 hours ago103December 04, 2023308apache-2.0Python
:rocket: Build and manage real-life data science projects with ease!
Bentoml5,9601211 hours ago119November 20, 2023182apache-2.0Python
Build Production-Grade AI Applications
Fate5,315114 hours ago33September 20, 2023813apache-2.0Python
An Industrial Grade Federated Learning Framework
Alternatives To Seldon Server
Select To Compare


Alternative Project Comparisons
Readme

Update January 2018

  • Seldon Core open sourced.
    • Seldon Core focuses purely on deploying a wide range of ML models on Kubernetes, allowing complex runtime serving graphs to be managed in production. Seldon Core is a progression of the goals of the Seldon-Server project but also a more restricted focus to solving the final step in a machine learning project which is serving models in production. Please have a look at the project page which includes extensive documentation to investigate further.

Seldon Server : * * Archived * *

This project is not actively maintained anymore please see Seldon Core.

Seldon Server is a machine learning platform that helps your data science team deploy models into production.

It provides an open-source data science stack that runs within a Kubernetes Cluster. You can use Seldon to deploy machine learning and deep learning models into production on-premise or in the cloud (e.g. GCP, AWS, Azure).

Seldon supports models built with TensorFlow, Keras, Vowpal Wabbit, XGBoost, Gensim and any other model-building tool — it even supports models built with commercial tools and services where the model is exportable.

It includes an API with two key endpoints:

  1. Predict - Build and deploy supervised machine learning models created in any machine learning library or framework at scale using containers and microservices.
  2. Recommend - High-performance user activity and content based recommendation engine with various algorithms ready to run out of the box.

Other features include:

  • Complex dynamic algorithm configuration and combination with no downtime: run A/B and Multivariate tests, cascade algorithms and create ensembles.
  • Command Line Interface (CLI) for configuring and managing Seldon Server.
  • Secure OAuth 2.0 REST and gRPC APIs to streamline integration with your data and application.
  • Grafana dashboard for real-time analytics built with Kafka Streams, Fluentd and InfluxDB.

Seldon is used by some of the world's most innovative organisations — it's the perfect machine learning deployment platform for start-ups and can scale to meet the demands of large enterprises.

Get Started

It takes a few minutes to install Seldon on a Kubernetes cluster. Visit our install guide and read our tech docs.

Community & Support

License

Seldon is available under Apache Licence, Version 2.0

Popular Kubernetes Projects
Popular Machine Learning Projects
Popular Virtualization Categories
Related Searches

Get A Weekly Email With Trending Projects For These Categories
No Spam. Unsubscribe easily at any time.
Python
Java
Docker
Deployment
Machine Learning
Deep Learning
Amazon Web Services
Cloud Computing
Kubernetes
Tensorflow
Azure
Microservices
Spark
Kafka
Google Cloud Platform
Recommendation System
Kafka Streams