August 23-34, 2022 - Virtual
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Tuesday, August 23 • 3:30pm - 4:10pm
MLSecOps with Automated Online and Offline ML Model Evaluations on Kubernetes - Tommy Li & Animesh Singh, IBM [Presented in English]

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MLSecOps is the intersection of machine learning, DevOps, infrastructure, and security. Machine Learning models can be easily reversed, leading to invaluable loss of Data. Having a robust MLSecOps infrastructure in place is absolutely necessary. It's incredibly complex for data scientists to monitor model security since it is hard to access and process model payloads on Kubernetes in scale. Therefore, it’s crucial to automate online real-time evaluations and detailed offline analysis on Kubernetes. Real-time evaluations such as model explanations can provide immediate feedback for each model prediction, while offline analysis such as fairness and adversarial detection can examine the model security over a period of time in order to visualize and report any potential threats in the model. This talk covers how to use KServe, Knative, Apache Kafka, and Trusted-AI tools to serve ML models, persist payloads, and automate both online and offline model evaluations in a production environment.

avatar for Tommy Li

Tommy Li

Senior Software Developer, IBM
Tommy Li is a senior software developer in IBM focusing on Cloud, Kubernetes, and Machine Learning. He is one of the Kubeflow committers and worked on various open-source projects related to Kubernetes, Microservice, and deep learning applications to provide advanced use cases on... Read More →
avatar for Animesh Singh

Animesh Singh

Distinguished Engineer and CTO - Watson Data and AI OSS Platform, IBM
Animesh Singh is CTO and Director for IBM Watson Data and AI Open Technology, responsible for Data and AI Open Technology strategy. Creating, designing and implementing IBM’s Data and AI engine for AI and ML platform, leading IBM`s Trusted AI efforts, driving the strategy and execution... Read More →

Tuesday August 23, 2022 3:30pm - 4:10pm EDT
  Open AI & Data Forum