Seldon Technologies Ltd was founded in 2014 with a simple aim: to accelerate the adoption of machine learning to solve some of the world’s most challenging problems. Seldon makes this happen by democratising technologies that were once the preserve of tech giants – and putting them in everyone’s hands.
Seldon’s first open-source release has grown into one of the world’s most popular machine learning platforms.
Seldon Core is an open-source machine learning deployment platform for Kubernetes that makes it easier and quicker to deploy machine learning models and experiments in production. Seldon Core enables data scientists and engineers to manage, serve and optimise models from any machine learning library or framework. It scales from start-up to enterprise-scale machine learning deployment scenarios and runs on any cloud provider, on customer’s own premises, or as a fully managed service.
The Seldon Core open-source project can be found on Github at https://github.com/SeldonIO/seldon-core
Seldon also runs the UK’s largest machine learning tools meetup, TensorFlow London, supported by Google: https://www.meetup.com/TensorFlow-London/
In the media
11th December 2019
Will machine learning create positive robots
their proposed structure uses the so-called " Seldon" algorithms named after the character Gary Seldon, created by science fiction writer Isaac Asimov. The latter invented "positronic robots" controlled by means of three
8th December 2019
Algorithm developed to train AI to avoid bad behaviors
Artificial intelligence (AI) has moved into the commercial mainstream thanks to the growing prowess of machine learning algorithms that enable computers to train themselves to do things like drive cars, control robots
6th December 2019
Researchers Build Framework To Avoid Machine Learning’s “Undesirable Outcomes”
Researchers at Stanford and the University of Massachusetts Amherst have introduced a framework for designing machine learning (ML) algorithms that make it easier for potential users to specify safety and fairness constraints.