Overview
Description
Qeexo AutoML is a fully automated, end-to-end machine learning (ML) platform that builds lightweight machine learning solutions (tinyML) running locally on constrained environments at the Edge. It augments the user experience and applicability of products like the RA family of 32-bit MCUs, adding intelligence with AI.
Features
- Supports Arm® Cortex™- M0 to M4 class MCUs like Renesas Synergy S5D9 and Renesas RA6M3
- Enables a wide range of machine learning methods, including GBM, XGBoost, Random Forest, Logistic Regression, CNN, RNN, ANN, Local Outlier Factor, and Isolation Forest
- Libraries generated from Qeexo AutoML are optimized for constrained endpoint device architectures: low latency, low power consumption, small footprint
- Automates tedious and repetitive machine learning processes – saves time/cost to production and eliminates room for error
- Zero coding necessary; machine learning expertise not required
Comparison
Applications
Applications
- Wearables
- Industrial
- Mobile
- IoT
- Automotive
- Smart home/appliances
Documentation
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| Type | Title | Date |
| Product Brief | PDF 341 KB | |
| Guide | PDF 4.05 MB | |
| Guide | PDF 2.54 MB | |
| Manual - Development Tools | PDF 4.08 MB | |
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Related Applications
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IoT Sensor Board with Machine Learning & Bluetooth Low Energy |
Videos & Training
Qeexo AutoML is Qeexo’s one-click, fully-automated platform that allows customers to leverage sensor data to rapidly build machine learning solutions for highly constrained environments with applications in industrial, IoT, wearables, automotive, mobile, and more.
In this video, we apply Qeexo AutoML to a motor control use case using the accelerometer sensor to detect non-normal vibration using the Renesas RA6M3 ML sensor module.
