October 12, 2021
Los Angeles, California + Virtual
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Tuesday, October 12 • 11:50am - 12:20pm
Defending Against Adversarial Model Attacks Using Kubeflow - Animesh Singh & Andrew Butler, IBM

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The application of AI algorithms in domains such as self-driving cars, facial recognition, and hiring holds great promise. At the same time, it raises legitimate concerns about AI algorithms robustness against adversarial attacks. Widespread adoption of AI algorithms where the predictions are hidden or obscured from the trained eye of the subject expert, opportunities for a malicious actor to take advantage of the AI algorithms grow considerably, necessitating the addition of adversarial robustness training and checking.  To protect against and mitigate the damages caused by these malicious actors,  this talk will examine how to build a pipeline that’s robust against adversarial attacks by leveraging Kubeflow Pipelines and integration with LFAI Adversarial Robustness Toolbox (ART). Additionally we will show how to test a machine learning model's adversarial robustness in production on Kubeflow Serving, by virtue of Payload logging (KNative eventing) and ART.

avatar for Animesh Singh

Animesh Singh

Distinguished Engineer and CTO - Watson Data and AI OSS Platform, IBM
Animesh Singh is a Distinguished Engineer and CTO for IBM Watson Data and AI Open Source Platform, responsible for Watson Platform OSS strategy and execution, driving IBM Watson-Red Hat Data and AI Open Source governance and technical roadmap, including customer and ISV engagements... Read More →
avatar for Andrew Butler

Andrew Butler

Developer - Deep Learning/Machine Learning/AI Advocate, IBM
Andrew Butler is a Machine Learning Software Developer for IBM, where he works on incorporating tools that increase trust in machine learning models by looking at the explainability, robustness, and fairness of those models. In addition, he works on a project that provides Kubernetes-style... Read More →

Tuesday October 12, 2021 11:50am - 12:20pm PDT
Room 502 AB + Online