There are many considerations before deploying deep learning models into the real world, especially in safety-critical environments like automated driving, smart medical devices, aerospace, and biomedical applications.
A deep learning researcher can achieve 99% accuracy on a deep learning model, but what about the edge cases? What if those edge cases represent someone’s life? Is AI ready to move from research to reality? Model accuracy is only one part of a production-ready system: model justification and documentation, rigorous testing, use of specialized hardware (GPUs, FPGAs, cloud resources, etc.), and collaboration between multiple people with various expertise related to the project and system.
In this session, we will discuss the importance of explainable models, system design and testing before an AI system is production-ready.