Meetup #33

Owned By Statistics: How Kubeflow & MLOps Can Help Secure ML Workloads

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 demonstrated 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 also talked about the direction for MLOps as an industry, and how we can use it to move faster, with less risk, than ever before.

In this episode

David Aronchick

David Aronchick

Program Manager, Azure Innovations, Azure

David leads works in the Azure Innovation Office on Machine Learning. This means he spends most of my time helping humans to convince machines to be smarter. He am only moderately successful at this. Previously, he led product management for Kubernetes on behalf of Google, launched Google Kubernetes Engine, and co-founded the Kubeflow project. He has also worked at Microsoft, Amazon and Chef and co-founded three startups. When not spending too much time in service of electrons, he can be found on a mountain (on skis), traveling the world (via restaurants) or participating in kid activities, of which there are a lot more than he remembers than when he was that age.

@aronchick

LinkedIn

Demetrios Brinkmann

Demetrios Brinkmann

Host

Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.