Podcasts & Blog2025-03-15T14:19:15+00:00

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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."

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 data.

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 the model on the embedded target.

Qeexo Adds Support for Arm’s Edge Processor

Qeexo, the “tinyML” specialist, said its AutoML platform now supports the smallest Cortex processors from Arm Ltd., making it the first vendor to automate machine learning on the Arm processor used for edge computing in sensors and microcontrollers.

The insideBIGDATA IMPACT 50 List for Q4 2020

The team here at insideBIGDATA is deeply entrenched in following the big data ecosystem of companies from around the globe. We’re in close contact with most of the firms making waves in the technology areas of big data, data science, machine learning, AI and deep learning. Our in-box is filled each day with new announcements, commentaries, and insights about what’s driving the success of our industry so we’re in a unique position to publish our quarterly IMPACT 50 List of the most important movers and shakers in our industry. These companies have proven their relevance by the way they’re impacting the enterprise through leading edge products and services. We’re happy to publish this evolving list of the industry’s most impactful companies!

Sensitivity Analysis with Qeexo AutoML

For machine learning models, Sensitivity parameter reflects on how sensitive the model is for classes under consideration. Sensitivity Analysis is generally performed before deployment of ML models in the real world application. The primary objective of the Sensitivity Analysis is to make ML model lean more towards certain class(es) than the other(s).

Live Classification Analysis

Qeexo AutoML enables machine learning application developers to do analysis of different performance met- rics for their use-cases and equip them to make decisions regarding ML models like tweaking some training parameters, adding more data etc. based on those real-time test data metrics. In this article, we will discuss in detail regarding live classification analysis module.

The insideBIGDATA IMPACT 50 List for Q1 2021

The team here at insideBIGDATA is deeply entrenched in following the big data ecosystem of companies from around the globe. We’re in close contact with most of the firms making waves in the technology areas of big data, data science, machine learning, AI and deep learning. Our in-box is filled each day with new announcements, commentaries, and insights about what’s driving the success of our industry so we’re in a unique position to publish our quarterly IMPACT 50 List of the most important movers and shakers in our industry.

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 [...]

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