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.

David leads Open Source Machine Learning Strategy at Azure. This means he spends most of his time helping humans to convince machines to be smarter. He is 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.

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MLOps Meetup #33 Owned By Statistics: How Kubeflow & MLOps Can Help Secure Your ML Workloads // David Aronchick - Head of Open Source ML Strategy at Azure 56:24 Share

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MLOps Meetup #33 Owned By Statistics: How Kubeflow & MLOps Can Help Secure Your ML Workloads // David Aronchick - Head of Open Source ML Strategy at Azure