Qeexo AutoML Demo: Automating Machine Learning for Embedded Devices

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Qeexo spun out of Carnegie Mellon University, has for a long time developed multi-touch technology for handset manufacturers which does ML on the device level. It has applied this approach to a new AutoML technology that allows companies to embed ML into hardware and conduct learning on sensor data. 

Qeexo AutoML is the company’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 mobile, IoT, wearables, automotive, and more.

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 hundreds of millions of consumer devices worldwide.

Delivering high performance, solutions built with Qeexo AutoML are optimized to have ultra-low latency, ultra-low power consumption, and an incredibly small memory footprint. As billions of sensors collect data on every device imaginable, Qeexo can equip them with machine learning to discover knowledge, make predictions, and generate actionable insights.

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