of Machine Learning
at the Edge
Qeexo develops machine learning solutions that generate actionable insights from sensor data.START FREE TRIAL
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.
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.
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.
The Qeexo AutoML platform is a great tool to enable ML-based features without [too much engineering] effort.
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.
FEATURED BLOG Sound Recognition with Qeexo AutoML
Introduction Sound Recognition is a technology based on traditional pattern recognition theories and signal analysis methods which is widely used in speech recognition, music recognition and many other research areas such as acoustical oceanography . Generally, microphones are regarded as sufficient sensing modalities as input to machine learning methods within these fields. Microphones are capable […]Zhongyu Ouyang and Dr. Geoffrey Newman 21 September 2020
PRESS Qeexo AutoML Enables Machine Learning on Arm Cortex-M0 and Cortex-M0+
First company to build an automated ML platform for the Arm Cortex-M0 and Cortex-M0+ processor MOUNTAIN VIEW, Calif. (PRWEB) September 09, 2020 Qeexo, developer of an automated machine learning (ML) platform that accelerates the deployment of tinyML...Qeexo, Co. 09 September 2020
BLOG Inference Settings: Instance Length and Classification Interval
Qeexo AutoML enables machine learning application developers to customize inference settings based on their use-case. These parameters are critical for achieving the best live performance of models on the embedded target. In this article, we will...Xun (Jared) Liu, Dr. Rajen Bhatt, and Dr. Geoffrey Newman
BLOG Classification Interval for Qeexo AutoML Inference Settings
Inference settings contain two important parameters; Instance length and Classification interval. In this blog, we will explain the Classification Interval and in conjunction with raw sensor signals, ODR, Instance length, latency, and performance of...Sidharth Gulati and Dr. William Levine 12 August 2020
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