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Lose the MCU: ST and Qeexo Simplify Machine Learning with AutoML

All About Circuits 19 May 2021

Intelligent decision-making has moved to the edge with machine learning, though deployment could be complicated. Qeexo and STMicroelectronics are looking to change that.

Read the full article here: https://www.allaboutcircuits.com/news/lose-the-mcu-st-and-qeexo-simplify-machine-learning-with-automl/

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Qeexo Collaborates with ST to Automate Machine Learning on Machine Learning Core (MLC) Sensors

Qeexo 18 May 2021

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 development of tinyML models for the edge, today announced that it is working with STMicroelectronics to enable developers to create machine learning models for ST’s Machine Learning Core (MLC) sensors, so that inferences can run right on the sensor, without the need for a microcontroller. This feature will be available to users in Q2.

Traditionally, due to limited computation power, memory size, and battery life, building machine learning solutions for edge devices had been challenging. Qeexo AutoML solves this. Its one-click, fully automated platform allows customers to rapidly build machine learning solutions for edge devices using sensor data. By moving machine learning to embedded processors and now sensors on edge devices, developers can improve privacy, latency, and availability.

“Qeexo continues to demonstrate technical leadership in the embedded machine learning space by automating machine learning on tiny, resource-constrained devices – this time, on a Machine Learning Core sensor, independent from an MCU,” said Sang Won Lee, CEO of Qeexo. “For use cases that can benefit from machine learning, but do not have access to MCUs due to cost, power, latency, or infrastructure constraints, there are significant advantages to running machine learning on the sensor, including cost and power savings.”

“Many IoT solutions developers are looking to easily add embedded machine learning to their very low power applications and need help to bridge the gap from concept to prototype to production,” said Simone Ferri, MEMS Sensors Division Director, STMicroelectronics. “We put MLC in our sensors to reduce system data transfer volumes and offload network processing. Qeexo AutoML can help unlock the benefits of inherently low-power sensor design, advanced AI event detection, wake-up logic, and real-time Edge computing.”

Qeexo also announced that it is launching a model converter that can take machine learning models in the ONNX format to optimize them for embedded devices. For customers who already have a machine learning team, and who have worked on and have existing machine learning models, they can use the Qeexo Model Converter to make them smaller and more optimized for embedded devices. The technology will also make it easier for developers who want to compare the performance of their hand-built models against the models automatically created with Qeexo AutoML.

In addition, Qeexo is now offering a machine learning consulting service to help clients jump-start their projects. Qeexo will first work with clients to develop and deploy commercial-ready machine learning solutions, then tailor Qeexo AutoML to fit customer needs. Qeexo will provide the knowledge transfer necessary for client teams to continue to use Qeexo AutoML for current and future projects.

About Qeexo
Qeexo is the first company to automate end-to-end machine learning for embedded edge devices (Cortex M0-M4 class). Our one-click, fully-automated Qeexo AutoML platform allows customers to leverage sensor data to rapidly build machine learning solutions for highly constrained environments with applications in industrial, IoT, wearables, automotive, mobile, and more. Over 300 million devices worldwide are equipped with AI built on Qeexo AutoML. Delivering high performance, solutions built with Qeexo AutoML are optimized to have ultra-low latency, ultra-low power consumption, and an incredibly small memory footprint.

For more information, go to www.qeexo.com.

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Building effective IoT applications with tinyML and automated machine learning

Embedded 27 January 2021

IoT enables continuous monitoring of environments and machines using tiny sensors. Advances in sensor technologies, microcontrollers, and communication protocols made mass production of IoT platforms, with many connectivity options, possible at affordable prices. Due to the low cost of IoT hardware, sensors are being deployed on a large scale at public places, residentials, and on machines.

Read the full article here: https://www.embedded.com/building-effective-iot-applications-with-tinyml-and-automated-machine-learning/

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The insideBIGDATA IMPACT 50 List for Q1 2021

insideBIGDATA 05 January 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. 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!

Read the full article here: https://insidebigdata.com/2021/01/05/the-insidebigdata-impact-50-list-for-q1-2021/

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Big Data Industry Predictions for 2021

InsideBigData 29 December 2020

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. In order to give our valued readers a pulse on important new trends leading into next year, we here at insideBIGDATA heard from all our friends across the vendor ecosystem to get their insights, reflections and predictions for what may be coming. We were very encouraged to hear such exciting perspectives. Even if only half actually come true, Big Data in the next year is destined to be quite an exciting ride. Enjoy!

Read the full article here: https://insidebigdata.com/2020/12/21/big-data-industry-predictions-for-2021/

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LETU Machine Learning Contest Video

KLTV 12 November 2020

Click HERE to view the video

Full URL to video source: https://www.kltv.com/video/2020/11/06/machine-learning-contest/

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Can a piece of drywall be smart? Bringing machine learning to everyday objects with TinyML

Diginomica 11 November 2020

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.”

Read the full article at: https://diginomica.com/can-piece-drywall-be-smart-bringing-machine-learning-everyday-objects-tinyml

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LeTourneau University students design artificial intelligence projects for contest

Longview News-Journal 09 November 2020

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.

The teams were challenged by contest sponsors Qeexo, the maker of the machine learning platform AutoML, and Arduino, an open-source electronics hardware and software platform, to provide solutions for real world problems using embedded machine learning. Students’ projects include a device that monitors hand movements to allow it to be almost unbeatable in a game of rock, paper, scissors to a another device that helps fly fishermen perfect their cast.

Link to the full article: https://www.news-journal.com/news/local/letourneau-university-students-design-artificial-intelligence-projects-for-contest/article_c936dc60-2088-11eb-930c-bb89584d78c8.html

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The insideBIGDATA IMPACT 50 List for Q4 2020

insideBIGDATA 13 October 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!

Read the full article here: https://insidebigdata.com/2020/10/13/the-insidebigdata-impact-50-list-for-q4-2020/

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Qeexo Adds Support for Arm’s Edge Processor

Datanami 12 October 2020

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.

Read the rest of the article here: https://www.datanami.com/2020/09/23/qeexo-adds-support-for-arms-edge-processor/