Why TinyML Applications Fail: An examination of common challenges and issues encountered for real-world projects
Christopher KNOROWSKI, CTO, SensiML Corp

Much has been made about the benefits of running TinyML models capable of executing at the IoT edge to reduce latency, privacy, performance, and reliability concerns of centralized cloud AI processing. Demos and proof-of-concepts have spanned a broad array of intelligent IoT applications from industrial predictive maintenance, to wake word triggering, agricultural monitoring, and image recognition to name a few.
Despite this, many users have encountered challenges in implementing TinyML for their real-world applications. In this talk we will discuss such issues drawing from over ten years of experience SensiML has had working with customers across a broad array of applications. This no-nonsense discussion will provide an unvarnished look at the various challenges encountered, common misconceptions about the technology, and various methods to address the common pitfalls that besiege commercial TinyML projects.

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