Google Cloud wants to make it easier for data scientists to share models
Today, Google Cloud announced Kubeflow pipelines and AI Hub, two tools designed to help data scientists put the models they create to work across their organizations. Rajen Sheth, director of product management for Google Cloud’s AI and ML products says that the company recognized that data scientists too often build models that never get used. He says that if machine learning is really a team sport, as Google believes, models must get passed from data scientists to data engineers and developers who can build applications based on them. To help fix that, Google is announcing Kubeflow pipelines, which are an extension of Kubeflow, an open source framework built on top of Kubernetes designed specifically for machine learning. Pipelines are essentially containerized building blocks that people in the machine learning ecosystem can string together to build and manage machine learning workflows. By placing the model in a container, data scientists can simply adjust the underlying model as n...