Qeexo AutoML

Enabling the New Era
of Machine Learning
at the Edge

Qeexo develops machine learning solutions that generate actionable insights from sensor data.

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2019 CES Innovation Awards
Honoree

2019 MWC Global Mobile Awards
Sortlisted

2020 AIconics Awards
Shortlisted

2021 CES Innovation Awards
Honoree

First automated ML platform for an Arm Cortex-M0/M0+

Supporting a wide range of machine learning algorithms, Qeexo AutoML is designed for lightweight, Cortex-M0-to-M4-class processors, yielding ultra-low power consumption and latency.

Automatically Build Machine Learning
Solutions with Sensor Data

Why Qeexo AutoML?

The Cortex-M0 and Cortex-M0+ processors pack high performance with very low power consumption, and the added support of the Qeexo AutoML platform enables application developers to easily add intelligence to small devices such as wearables, making a world of one trillion intelligent devices a closer reality.

Steve Roddy, Vice President of Product Marketing, Machine Learning Group Arm

Combined with Arduino Nano 33 IoT, users [of Qeexo AutoML] can quickly create smart IoT sensors that can perform analytics at the edge, minimize communication, and maximize battery life.

Dominic Pajak, VP Business Development Arduino

By automating the development of ML solutions for advanced industrial IoT applications such as condition monitoring and predictive maintenance, Qeexo AutoML eases the usability of our products.

Pierrick Autret, Product Marketing Engineer STMicroelectronics

The Qeexo AutoML platform is a great tool to enable ML-based features without [too much engineering] effort.

Qeexo AutoML User

The UI is clean, intuitive, and provides end-to-end model deployment support. We no longer have to fumble with various tools for collecting data, building models, and deploying solutions.

Qeexo AutoML User

Past Events

ST Technology Tour Boston

TIME: Nov 2nd, 2022 @ 9 AM EST

SPEAKER: Stephanie Pavlick

DETAILS: In this session we will show you how to use Qeexo AutoML to easily collect data, train models, and deploy working models to ST Machine Learning Core (MLC) without writing any code.

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