Qeexo is the first company to automate end-to-end machine learning for edge devices (e.g. Cortex M0-M4 class MCUs). Its Qeexo AutoML platform provides an intuitive UI that allows users to collect, clean, and visualize sensor data and automatically build machine learning models using different algorithms. Delivering high performance, solutions built with Qeexo AutoML are optimized to have ultra-low latency and power consumption, and an incredibly small memory footprint – so tiny it can run on a Cortex-M0+! Many equate machine learning with deep learning (neural networks). However, deep learning is often not the best answer, and definitely not the only answer, when it comes to constrained environments at the Edge. Qeexo AutoML can apply 17 (and counting) different machine learning algorithms, including both neural networks and non-neural-networks, to the same dataset, while generating metrics for each (accuracy, memory size, latency), so that users can pick the model that best fits their data and application. Qeexo AutoML greatly simplifies the machine learning model building process, with its one-click, fully automated workflow, eliminating room for errors. Libraries built with Qeexo AutoML run locally on Edge devices without having to go to the Cloud, resulting in millisecond-latency, high availability, privacy, and security, as well as reduced power, bandwidth and infrastructure costs.
Qeexo AutoML: Automating Machine Learning for Embedded Devices
Leveraging sensor data, Qeexo AutoML is an automated, end-to-end machine learning platform that creates machine learning models for embedded devices.
Arm Tech Talk
Automate tinyML Development & Deployment with Qeexo AutoML
What's the smallest machine learning model you've built? Qeexo's is so small, it can even run on an Arm Cortex M0+!
Automatically Build tinyML Solutions on Embedded Devices
See how easy it is to automate "tinyML" machine learning development for sensor modules with Arm Cortex-M-0-to-Cortex-M-4 microprocessors!