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This session will talk about to evaluate and add trustworthiness and fairness to your multi-cloud Kubeflow workflows. The session will describe the following: * Overview of responsible AI & fairness. * What are the different aspects of fairness. * How to evaluate fairness in the input data & the model. * Demo: Using open source tools such as MinDiff/Tensorflow/ai-widgets for fair model learning * Demo: Including fairness as a parameter in HyperParameter tuning in Kubeflow * Transition from experimentation in Notebooks to ML production pipelines. * Recommendations to ensure your AI workflows are trustworthy and fair. Suggestions for enhancements in Kubeflow that can accelerate this evolution.
Meenakshi Kaushik leads product management for NIM Operator and KServe.. Meenakshi is interested in the AI and ML space and is excited to see how the technology can enhance human well-being and productivity.
