Open Source Summit + ELC North America 2020

While machine learning is spreading like wildfire, very little attention has been paid to the ways that it can go wrong when moving from development to production. Even when models work perfectly, they can be attacked and/or degrade quickly if the data changes. Having a well understood MLOps process is necessary for ML security!

Using Kubeflow, we will demonstrate how to the common ways machine learning workflows go wrong, and how to mitigate them using MLOps pipelines to provide reproducibility, validation, versioning/tracking, and safe/compliant deployment. We will also talk about the direction for MLOps as an industry, and how we can use it to move faster, with less risk, than ever before.