Qeexo has announced the launch of its AutoML product, a one-click, fully automated platform that allows customers to rapidly build machine learning solutions for Edge devices using sensor data. Qeexo has selected the Arm Cortex-M0-M4 class MCUs as the first hardware targets to be supported by Qeexo AutoML. At launch, Qeexo AutoML will support STMicroelectronics’s SensorTile.box, a compact multi-sensor module which includes the Cortex-M4 MCU and will continue to augment support for other hardware platforms.
Machine learning is moving to embedded processors on edge devices, improving privacy, latency, and availability. However, given limited computation power, memory size, and battery life, building machine learning solutions for edge devices is challenging. Achieving commercial-grade performance requires a team of difficult-to-hire machine learning engineers who devote their time to: preprocess data, extract features, select models, optimize hyperparameters, validate results, and deploy models to target. Even for experts, this is a lengthy, error prone, and repetitive process.
With its one-click, fully automated workflow, Qeexo AutoML greatly simplifies the machine-learning-solution development process and eliminates room for errors. All the complicated machine learning tasks are automated by Qeexo AutoML. Machine learning engineers can now focus their time on mission-critical R&D instead of performing tedious, repetitive steps. In addition, Qeexo AutoML eliminates the need for companies to invest in expensive, in-house machine learning teams, resulting in huge time and cost savings.
Qeexo AutoML is based on the same machine learning platform that Qeexo developed as the basis for its FingerSense, EarSense, and TouchTools products, which are commercialized on over 210 million consumer devices worldwide.
Qeexo is the first company to automate end-to-end machine learning for embedded Edge devices (Cortex M0-M4 class). Qeexo AutoML platform allows customers to leverage sensor data to rapidly build machine learning solutions for highly constrained environments with applications in mobile, IoT, wearables, automotive, and more.
Spun out of Carnegie Mellon University, Qeexo is venture-backed and headquartered in Mountain View, CA, with offices in Pittsburgh, Shanghai, and Beijing.
To learn more, visit http://automl.qeexo.com.