Whileone Tech Space

Inspiration In high-throughput production environments, every sensor reading tells a story. Test systems continuously record Pressure , Temperature , and Differential Loss (DL)  across thousands of cycles, but much of this data remains passive, observed but not interpreted. We set out to change that by deploying machine learning directly at the edge  on a BeagleBone Black  board. The goal was not anomaly detection, but live inference : to compute what the ideal DL should be (

In the first part of the blog, we described the problem that we intend to solve, the data gathering, post processing, and generating the final training data. In the 2nd part, we will take a look at the Machine Learning model we used for training and for inference with new data. Correlation between various counters We have captured various counters for various benchmarks. Here is a graph that shows the correlation between each counter with every other counter. ​ K Neighbors Re