To train a model, you must have a dataset in the CSV format. A typical dataset contains independent features (predictors) and dependent variables (target or label) which the model is learning to predict. Each row of data represents feature values, and the first row is used for column names. The data can be either fully prepared or it can be raw sensor data that requires preprocessing.
For signal processing, the platform provides a wide range of options, including windowing, feature extraction, and feature selection.
To build the most compact models with high inference speed and optimal accuracy, you can control model settings. Model accuracy is calculated automatically on the split portion of the training dataset. Additionally, you can upload an independent dataset to perform validation and calculation of metrics.
During training, the model is looking for patterns and dependencies between the predictors and the target variable. Once the training is complete, you will receive an archive with a ready-to-use Model and a calculation of the total footprint of the entire ML solution. You can embed the model in the device or evaluate the model's quality on test data using the NRF EdgeAI Desktop Inference Runner, which is located in the artifacts folder of the same downloadable archive.
The machine learning workflow and pipeline within Edge AI Lab consists of the following steps:
- Selecting data for training
- Training your model
- Running inference on a device
In addition, Edge AI Lab includes an AI chatbot to help you quickly find the information you need. If you encounter any issues, you can submit a support ticket through DevZone.