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September 27-30, 2021
Seattle, Washington, USA + Virtual
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Wednesday, September 29 • 10:30am - 11:20am
(VIRTUAL) Infusing Trusted AI using Machine Learning Payload Logging on Kubernetes - Tommy Li & Andrew Butler, IBM

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As more machine learning models are developed and served on Kubernetes, it's becoming harder to track the incoming data and payloads by just reading the logs. For trusting model predictions, drift, anomaly, adversarial and bias detection need to be built in the platform. Data scientists have difficulty figuring out model behavior on Kubernetes since it's hard to access and process model payloads on Kubernetes. Therefore, it's important to record and persist the model input and output payloads with the proper schema. These payloads can be used with other tools to explain, analyze, and generate machine learning metrics such as fairness and drift detection for models running in production. This can help AI operators and data scientists visualize and find any potential issues with the model. This talk covers how to use KFServing, Kafka Connect, and AIF360 to serve ML models, persist payloads, and measure the model fairness in a production environment.

Speakers
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 →
avatar for Tommy Li

Tommy Li

Senior Software Engineer, IBM
Tommy Li is a 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 cloud-computing... Read More →



Wednesday September 29, 2021 10:30am - 11:20am PDT
MeetingPlay Platform + Virtual Learning Lab
  AI & Data, Trusted and Responsible AI