Introducing Seldon Core -- Machine Learning Deployment for Kubernetes

Seldon Core focuses on solving the last step in any machine learning project to help companies put models into production, to solve real-world problems and maximise the return on investment. Data scientists are freed to focus on creating better models while devops teams are able to manage deployments more effectively using tools they understand. Instead of just serving up single models behind an API endpoint, Seldon Core allows complex runtime inference graphs to be deployed in containers as microservices. Efficiency -- traditional infrastructure stacks and devops processes don't translate well to machine learning, and there is limited open-source innovation in this space, which forces companies to build their own at great expense or to use a proprietary service. Also, data engineers with the necessary multidisciplinary skillset spanning ML and ops are very scarce.