Training Multi-Modal ML Classification Models for Real-Time Detection of Debilitating Disease

Multi-modal machine learning (ML) is revolutionizing the healthcare industry by enabling real-time disease detection through simultaneous video and audio data analysis. This session will walk attendees through the complete process of developing classification models that combine video and audio inputs to detect early signs of debilitating diseases. By deconstructing the model-building process, we’ll explore how to curate and preprocess multi-modal datasets, select appropriate model architectures, and effectively train models that can analyze complex medical data in real time.

The session goes beyond theory with practical, hands-on guidance for building these models. Attendees will gain insights into creating video and audio classifiers from scratch and learn how to fuse these models for more accurate, real-time predictions. We will also demonstrate how to deploy these models in a live setting, showing how real-time classification can be achieved. Participants will leave with working code, a clear methodology for approaching multi-modal ML problems, and a roadmap to build models for healthcare or other domains requiring multi-modal analysis.