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AutoML Mentioned in insideBIGDATA Latest News 6/12
Qeexo Announces General Availability of the Qeexo AutoML Platform to Enable TinyML for Edge Devices
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."
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).
Qeexo Takes Misery Out of EdgeML
Startup Takes a Dose of its Own Medicine
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.
Inference Settings: Instance Length and Classification Interval
Qeexo AutoML enables machine learning application developers to customize inference settings based [...]
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 [...]
Sound Recognition with Qeexo AutoML
Introduction Sound Recognition is a technology based on traditional pattern recognition theories [...]
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.
LeTourneau University students design artificial intelligence projects for contest
Since mid-September, 10 teams of LeTourneau University engineering students have been working on projects involving artificial intelligence to enter into a contest. Winners of that contest were announced Friday in the lobby of the Glaske Engineering Center after demonstrations from students.
Can a piece of drywall be smart? Bringing machine learning to everyday objects with TinyML
So-called smart devices like Amazon Echo and Google Nest made early headway into our homes. But will devices as small as a vibration sensor soon outsmart an Echo? Here's a look under the hood of "TinyML."
LETU Machine Learning Contest Video
Click HERE to view the video Full URL to video source: https://www.kltv.com/video/2020/11/06/machine-learning-contest/
Big Data Industry Predictions for 2021
2020 has been year for the ages, with so many domestic and global challenges. But the big data industry has significant inertia moving into 2021.
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.
Building effective IoT applications with tinyML and automated machine learning
IoT enables continuous monitoring of environments and machines using tiny sensors. Advances [...]
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 [...]
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 [...]
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.
Tree Model Quantization for Embedded Machine Learning Applications
This blog post is a companion to my talk at tinyML Summit [...]