When building machine learning applications, it’s essential to create tight feedback loops between development and testing. Developers need to see the impact of their changes on real world performance so they can evolve their datasets and algorithms accordingly. For example, if you’re building a keyword spotting application, it’s critical to understand how well keywords are detected in a continuous stream of audio.
This is a companion discussion topic for the original entry at https://www.edgeimpulse.com/blog/announcing-performance-calibration