The model training process in Neuton contains the following steps:

A Virtual Machine (VM) is automatically provisioned to perform the following tasks: data preprocessing, feature engineering, model training, validation, and preparation of the C Library for predictions. This process is fully automated and does not require any user input or action.

Digital Signal Processing (DSP) for Sensor Data

This option makes data suitable for creating models. DSP consists of windowing, feature extraction, and feature selection. The user can flexibly choose the needed settings.

During this step, our proprietary neural network algorithm, Neuton, will automatically create a neural network architecture to achieve the best possible score on the validation data (measured by the metric selected by the user).

Once model training is complete, a downloadable archive is generated. It contains everything you need to embed the model into an MCU or perform a prediction in the user's infrastructure. It is also possible to perform predictions directly on the platform.

Upon completion of training, the virtual machine will be automatically deprovisioned.