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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 09 September 2020 -
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 -
BLOG Anomaly Detection in Qeexo AutoML
Qeexo AutoML supports three one-class classification algorithms widely used for anomaly/outlier detection; Isolation Forest, Local Outlier Factor, and One-class Support Vector Machine. These algorithms build models by learning from only one class of...
Dr. Karanpreet Singh and Dr. Rajen Bhatt 15 July 2020 -
PRESS Qeexo Takes Misery Out of EdgeML
Startup Takes a Dose of its Own Medicine
Electronic Engineering Journal 14 July 2020 -
PRESS Qeexo AutoML Now Hosted on AWS, Adds Algorithm Support
The latest release of Qeexo AutoML makes the automated TinyML model development and deployment platform available as a web application hosted on Amazon Web Services (AWS).
Embedded Computing 16 June 2020 -
PRESS Qeexo Takes ‘TinyML’ to AWS Cloud
"Qeexo, the Carnegie Mellon University spinoff, is expanding public cloud access to its automated machine learning platform as it pushes its no-code “TinyML” approach to the network edge."
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