Qeexo AutoML selected as a 2021 CES Innovation Awards Honoree!
Qeexo AutoML shortlisted for the 2020 AIconics Awards!
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 Introducing Qeexo Model Converter
Our latest API service for fitting your existing ML models onto an embedded target as small as a Cortex-M0+! Qeexo AutoML offers end-to-end machine learning with no coding required. While this SaaS product presents a wholistic user experience, we understand that machine learning (ML) practitioners working in the tinyML space may want to use their preexisting models that they’ve already spent a lot of time and efforts to finetune. To these folks working on tinyML applications, fitting the models onto embedded hardware with constrained resources is the final step before they can test their models on the embedded Edge device. However, this step requires a specialized set […]Gilbert Tsang, Director of Product Management 29 April 2021
BLOG Tree Model Quantization for Embedded Machine Learning Applications
This blog post is a companion to my talk at tinyML Summit 2021. The talk and this blog overlap in some content areas, but each also has unique content that complements the other. Please check out the video if you are interested. Why Quantization?...Dr. Leslie J. Schradin, III 28 May 2021
PRESS Lose the MCU: ST and Qeexo Simplify Machine Learning with AutoML
Intelligent decision-making has moved to the edge with machine learning, though deployment could be complicated. Qeexo and STMicroelectronics are looking to change that.All About Circuits 19 May 2021
PRESS Qeexo Collaborates with ST to Automate Machine Learning on Machine Learning Core (MLC) Sensors
Company also debuts the Qeexo Model Converter to optimize customers’ existing machine learning models for the Edge MOUNTAIN VIEW, CALIF. (PRWEB) MAY 18, 2021 Qeexo, developer of an automated machine learning (ML) platform that accelerates the...Qeexo 18 May 2021
Automate tinyML Development & Deployment with Qeexo AutoML
- March 9, 2021 8:00 am PST, 4:00 pm GMT, 10:00 am CST
- Tina Shyuan, Director of Product Marketing at Qeexo
Join Tina for a hands-on workshop of how to automatically build multiple ML models with Qeexo AutoML on the ST SensorTile.box!
Truly Smart Interactivity with Sensors and ML at the Edge
- April 15, 2021 9:10 am PST, 4:10 pm GMT, 10:10 am CST
- Chris Harrison, CTO
Come listen to our CTO, Chris Harrison, speak about enabling smart sensors at the Edge with machine learning at MEMS & Sensors Technical Congress (MSTC).
Register for a free evaluation or other SaaS options.
Interested in leveraging sensor data from your devices? We're happy to help!
Submit your information and we will get in touch with you.