Kubeflow 1.0 Brings a Production-Ready Machine Learning Toolset to Kubernetes - The New Stack

For developers looking to more easily parallelize (and more) their machine learning (ML) workloads using Kubernetes, the open source project Kubeflow has reached version 1.0 this week. The now production-ready offers "a core set of stable applications needed to develop, build, train, and deploy models on Kubernetes efficiently." The project was first open sourced in December 2017 at KubeCon CloudNativeCon and has since grown to hundreds of contributors from more than 30 participating organizations such as Google, Cisco, IBM, Microsoft, Red Hat, Amazon Web Services and Alibaba. Alongside the blog post from the Kubeflow team itself, Google has offered a post on how Kubeflow works with Anthos, while IBM's Animesh Singh explores the "highlights of the work where we collaborated with the Kubeflow community leading toward an enterprise-grade Kubeflow 1.0." In an interview with The New Stack, Singh explained the origins of Kubeflow as one attempting to simply bring TensorFlow to Kubernetes.