I like to track the Inference performance metrics using MLflow (optional wandb).
So far I see EI API is the way to go to obtain the performance metrics. Correct?
You need to provide some Body parameters. Are these related to an audio application? Is it possible to provide some background about the meaning of these body parameters?
The case study that I am working on is a “motion” application, using IMU data and it is a regression problem.
To goal is to perform a similar study as in Inference performance metrics, but this for different datasets, models, signal processing approaches and embedded devices (Unoptimized vs. Quantized).
An approach is using the results of the EON tuner. However, currently I am setting up a data/ML-pipeline (MLflow for metrics/artifact tracking) on a local machine were I also will train some models. For comparison it could be of interest to obtain also metrics from these models.
Tips & ideas are welcome.
Note. In Deployment, EI platform gives: RAM, Latency, flash and accuracy. Currently I am using the EI API to obtain the needed data for the calculation of MSE. Could be of interest, for regression, to add Mean Squared Error (MSE) (or Mean Absolute Error (MAE) to the table, instead of accuracy