Summary
Building Robust Data Infrastructure
Multi-modal AI refers to the ability of an AI system to process and integrate multiple types or “modalities” of data, including imaging, video, genomics, audio, and more. As these systems evolve to incorporate more modalities, the need for robust, scalable, and trustworthy data infrastructure becomes critical. However, such applications face complex challenges related to processing, storage, governance and secure distribution of large and sensitive datasets. Vendors may offer cloud-based portal solutions, however these are often too platform-specific and AI integration becomes limited. Manual steps are still required to ensure datasets can be directed towards these systems.
The alternative approach relies on organizations developing independent data repositories, inferencing services, and machine learning and training. While allowing for more seamless AI pipelining and integration with commercial and open-source systems, it requires significant operational complexity.
AI-Ready Data Trusts
To create a seamless method for managing and processing multi-modal data, CAAI has developed and deployed AI-ready data platforms that use an agent-based system coupled with open-source, automated machine learning. Furthermore, review tools allow dynamic load-balancing and cross-network operation, as well as the development of research and clinical AI models using data managed within the platform.