De-risking Embedded Machine Learning Projects Using Novel Technologies

Explore a partner session from tinyML Asia 2021 focused on de-risking embedded machine learning projects using novel technologies. Learn about the challenges faced in machine learning projects, especially in embedded systems, and discover the design and engineering philosophy employed by Edge Impulse to ensure customer success. Gain insights into device constraints, object detection, data sets, test data set performance, and realistic application testing. Understand how to overcome the high failure rate of machine learning projects and apply these principles to embedded systems for improved outcomes.

Introduction
The problem
Device constraints
Object detection
Data sets
Test data set performance
Realistic application testing
Conclusions
Contact us
Conclusion
Sponsors