In the latest series of TinyML UK talks, I recently presented on how to make ML work in the real world.

In this talk, I share elements of our product design process, share some amusing stories from deploying ML models to real-world environments, see how the real world can throw up surprising, unexpected and plain strange scenarios, and propose tools and techniques to help build ML models that really work.

While ML researchers continue to deliver new techniques and tools to improve the performance of ML-based systems, there’s often a gap between those developments and ML systems working in the real world: why is it often the case that when ML models are deployed in the real they do not work anywhere near as well as lab experiments suggest? What is it that leads to this disconnect?

In case you missed it, you can watch the full talk below, or on the tinyML YouTube channel and access to the presentation slides here.


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 About Audio Analytic 

Audio Analytic is the pioneer of AI sound recognition technology. The company is on a mission to give machines a compact sense of hearing. This empowers them with the ability to react to the world around us, helping satisfy our entertainment, safety, security, wellbeing, convenience, and communication needs across a huge range of consumer products.

Audio Analytic’s ai3™ and ai3-nano™ sound recognition software enables device manufacturers to equip products at the edge with the ability to recognize and automatically respond to our growing list of sounds and acoustic scenes.