Seldon Core 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. Weave Cloud works alongside machine learning platforms such as Seldon's. In this tutorial, you will deploy a predictive service that recognizes drawn numbers from 0 to 9. The predictive model was created using TensorFlow. This example describes how to deploy the pre-packaged Docker image that is available in the Seldon server.
We just finished Barclays Accelerator London, the world's leading fintech accelerator programme powered by Techstars. It was such an honour to share what Seldon has been building at Demo Day on 18th April 2016. Pitching to a 1000-strong audience at the O2 was a fitting finale to an incredible 13-week Techstars programme -- we all learned so much, connected with hundreds of great people, and established a new market for Seldon in the finance sector. We were delighted to announce a project with Barclaycard to help identify customers that default after receiving credit increases. This could not only save millions in missed payments, but would also result in fewer customers in financial difficulty.
We introduce a modular system that can be deployed on any Kubernetes cluster for question answering via REST API. This system, called Katecheo, includes four configurable modules that collectively enable identification of questions, classification of those questions into topics, a search of knowledge base articles, and reading comprehension. We demonstrate the system using publicly available, pre-trained models and knowledge base articles extracted from Stack Exchange sites. However, users can extend the system to any number of topics, or domains, without the need to modify any of the model serving code. All components of the system are open source and available under a permissive Apache 2 License.