  Deep Learning models are getting more and more popular but constraints on
explainability, adversarial robustness and fairness are often major concerns
for production deployment. Although the open source ecosystem is abundant on
addressing those concerns, fully integrated, end to end systems are lacking in
open source. Therefore we provide an entirely open source, reusable component
framework, visual editor and execution engine for production grade machine
learning on top of Kubernetes, a joint effort between IBM and the University
Hospital Basel. It uses Kubeflow Pipelines, the AI Explainability360 toolkit,
the AI Fairness360 toolkit and the Adversarial Robustness Toolkit on top of
ElyraAI, Kubeflow, Kubernetes and JupyterLab. Using the Elyra pipeline editor,
AI pipelines can be developed visually with a set of jupyter notebooks.
