Building A Machine Learning Platform With Kubeflow And Ray On Google Kubernetes Engine - cyberpogo

To start building an ML Platform, you should support the basic ML user journey of notebook prototyping to scaled training to online serving. If your organization has multiple teams, you may additionally need to support administrative requirements of multi-user support with identity-based authentication and authorization. Two popular OSS projects – Kubeflow and Ray – together can support these needs. Kubeflow provides the multi-user environment and interactive notebook management. Ray orchestrates distributed computing workloads across the entire ML lifecycle, including training and serving.