cover of episode The Metrics Revolution: Scaling

The Metrics Revolution: Scaling

2024/5/17
logo of podcast Machine Learning Tech Brief By HackerNoon

Machine Learning Tech Brief By HackerNoon

Shownotes Transcript

This story was originally published on HackerNoon at: https://hackernoon.com/the-metrics-revolution-scaling). Identify the metrics that are agnostic of the form factor - these are core metrics for the conversational AI agent in question.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning). You can also check exclusive content about #ai), #google-assistant), #conversational-ai), #user-perceived-metrics), #user-reported-metrics), #reliability-and-latency), #ground-truth-metric), #production-metric), and more.

        This story was written by: [@pmukherjee](https://hackernoon.com/u/pmukherjee)). Learn more about this writer by checking [@pmukherjee's](https://hackernoon.com/about/pmukherjee)) about page,
        and for more stories, please visit [hackernoon.com](https://hackernoon.com)).
        
            
            
            Discusses a simple solution for scaling performance evaluation infrastructure to multiple work factors. I am also the first author patent holder in this area - Standardizing analysis metrics across multiple devices. https://patents.google.com/patent/US20240031261A1/en

Identify the metrics that are agnostic of the form factor - these are core metrics for the conversational AI agent in question. Identify the logging signals needed for instrumenting these metrics. Adding mapping configuration for the corresponding logging signals on each form factor. Transform the form factor specific logging signals to an uniform space which is agnostic of the form factor. The metric instrumentation framework will only be based on the uniform logging signals.