Kubernetes Gets an Automated ML Workflow

A stable version of an automation tool released this week aims to make life easier machine learning developers training and scaling models, then deploying ML workloads atop Kubernetes clusters. Roughly two years after its open source release, Kubeflow 1.0 leverages the de facto standard cluster orchestrator to aid data scientists and ML developers in tapping cloud resources to run those workloads in production. Among the stable workflow applications released on Monday (March 2) are a central dashboard, Jupyter notebook controller and web application along with TensorFlow and PyTorch operators for distributed training. Contributors from Google, IBM, Cisco Systems, Microsoft and data management specialist Arrikto said Jupyter notebooks can be used to streamline model development. Other tools can then be used to build application containers and leverage Kubernetes resources to train models.