Google started the open-source Kubeflow Project with the goal of making Kubernetes the best way to run machine learning (ML) workloads in production. Today, Kubeflow 1.0 was released. Kubeflow helps companies standardize on a common infrastructure across software development and machine learning, leveraging open-source data science and cloud-native ecosystems for every step of the machine learning lifecycle. With the support of a robust contributor community, Kubeflow provides a Kubernetes-native platform for developing, orchestrating, deploying, and running scalable and portable ML workloads. Using Kubeflow on Google Cloud's Anthos platform lets teams run these machine-learning workflows in hybrid and multi-cloud environments while taking advantage of Google Kubernetes Engine's (GKE) enterprise-grade security, autoscaling, logging, and identity features.