Embedded Machine Learning in the Real World

Explore embedded machine learning applications in the real world through this insightful conference talk by Daniel Situnayake, Founding tinyML Engineer at Edge Impulse. Delve into the practical aspects of implementing machine learning on embedded devices, covering topics such as bandwidth, latency, and economics. Discover real-world use cases and gain an understanding of the current state-of-the-art in tiny models and accelerated hardware. Learn about the available tooling and explore future opportunities in this rapidly evolving field. Gain valuable insights into the challenges and potential of embedded machine learning from an industry expert.

Introduction
Overview
Bandwidth Latency Economics
Real World Use Cases
Practical State of the Art
Tiny Models
Accelerated Hardware
Tooling
Opportunities
Future
Outro