SW platform that allows users to collect, clean, and visualize sensor data and automatically build machine learning models.
Qeexo AutoML allows users to leverage sensor data to rapidly build “tinyML” solutions for highly constrained environments with applications in industrial IoT, wearables, automotive, mobile, and more.
Key Features:
- Automates the complex and labor-intensive processes of a typical machine learning workflow – no coding or ML expertise required!
- An end-to-end solution that embeds the data science, machine learning, signal processing, optimization, and embedded engineering needed to deliver AI algorithms for endpoint/edge devices – no need to switch between complicated tools
- Enables 17 different ML algorithms: GBM, XGBoost, Random Forest, Logistic Regression, Gaussian Naive Bayes, Decision Tree, Polynomial SVM, RBF SVM, SVM, CNN, RNN, CRNN, ANN, Local Outlier Factor, One Class SVM, One Class Random Forest, Isolation Forest.
- Augmented with an easy-to-use interface for labeling, recording, validating, and visualizing time-series sensor data
- Automatically performs feature extraction and selection
- Built-in quantization and model compression to reduce size of models
- Translates machine learning models into C code for ease of deployment to target device
- Models generated by Qeexo AutoML perform inference on-device and are optimized for constrained environments: low latency, low power consumption, small footprint
- Supports Arm® Cortex™- M0 to M4 class MCUs usually considered too small to run machine learning
- Supports ST Sensors with Machine Learning Core (LSM6DSOX, ISM330DHCX) for further power savings
