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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/

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Qeexo AutoML Enables Machine Learning on Arm Cortex-M0 and Cortex-M0+

Qeexo, Co. 09 September 2020

First company to build an automated ML platform for the Arm Cortex-M0 and Cortex-M0+ processor

MOUNTAIN VIEW, Calif. (PRWEB) September 09, 2020

Qeexo, developer of an automated machine learning (ML) platform that accelerates the deployment of tinyML at the edge, today announces that its Qeexo AutoML platform now supports machine learning on Arm® Cortex®-M0 and Cortex®-M0+ processors, which power devices including sensors and microcontrollers from companies such as Arduino, Renesas, STMicroelectronics, and Bosch Sensortec.

The Arm Cortex-M0 processor is the smallest Arm processor available, and the Cortex-M0+ processor builds on Cortex-M0 while further reducing energy consumption and increasing performance. Qeexo is the first company to automate adding machine learning to a processor of this size. The Cortex-M0 and Cortex-M0+ processors are designed for smart and connected embedded applications, and are ideal for use in simple, cost-sensitive devices due to the lower power-consumption and ability to extend the battery life of critical use cases such as activity trackers.

Machine learning models built with Qeexo AutoML are highly optimized and have an incredibly small memory footprint. Models are designed to run locally on embedded devices, ideal for ultra low-power, low-latency applications on MCUs and other highly constrained platforms.

“This integration delivers the advantages of data processing at the edge to even the smallest of devices,” said Sang Won Lee, co-founder and CEO of Qeexo. “Qeexo AutoML, combined with the accessibility of MCUs from companies such as Arduino, Renesas, STMicroelectronics, and Bosch Sensortec, greatly benefits application developers, who can now build smart hardware products with relative ease.”

The growing list of machine learning algorithms supported on Qeexo AutoML currently include: GBM, XGBoost, Random Forest, Logistic Regression, Decision Tree, SVM, CNN, RNN, CRNN, ANN, Local Outlier Factor, and Isolation Forest. Several hardware platforms from Arduino, Renesas, and STMicroelectronics work with Qeexo AutoML out-of-the-box.

Supporting Partner Quotes

Arm

“Today even the smallest devices can contain some layer of artificial intelligence and machine learning. 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.”

— Steve Roddy, Vice President of Product Marketing, Machine Learning Group of Arm

Arduino

“Arduino is on a mission to make machine learning simple enough for anyone to use. We’re excited to partner with Qeexo AutoML to accelerate professional embedded ML development by guiding users to the optimal algorithms for their application. Combined with Arduino Nano 33 IoT, users can quickly create smart IoT sensors that can perform analytics at the edge, minimize communication, and maximize battery life.”

– Dominic Pajak, VP Business Development, Arduino

Bosch

“Bosch Sensortec and Qeexo are collaborating on machine learning solutions for smart sensors and sensor nodes. We are glad that Qeexo’s AutoML has added support for Cortex-M0 families, to which Bosch Sensortec’s smart sensors like BMF055 belongs. We are excited to see more applications made possible by combining the smart sensors from Bosch Sensortec and AutoML from Qeexo.”

– Marcellino Gemelli, Director of Global Business Development at Bosch Sensortec

Renesas

“Renesas and Qeexo collaborated on the design of a new RA-Ready sensor board: the RA6M3 ML Sensor Module. Equipped with various motion and environmental sensors and enhanced with Qeexo AutoML, this sensor module is the perfect reference platform for developing intelligent machine learning applications.”

– Kaushal Vora, Director of Strategic Partnerships & Global Ecosystem at Renesas

STMicroelectronics

“Qeexo AutoML recently added support for our STWIN industrial platform, which features embedded industrial-grade sensors and an ultra-low-power microcontroller for vibration analysis. 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.”

– Pierrick Autret, Product Marketing Engineer at STMicroelectronics

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About Qeexo
Qeexo is the first company to automate end-to-end machine learning for embedded edge devices (Cortex-M0-to-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, mobile, automotive, and more.

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. As billions of sensors collect data on every device imaginable, Qeexo can equip them with machine learning to discover knowledge, make predictions, and generate actionable insights.

Spun out of Carnegie Mellon University, Qeexo is venture-backed and headquartered in Mountain View, CA, with offices in Pittsburgh, Shanghai, and Beijing. To learn more, visit http://www.qeexo.com.

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Qeexo Takes Misery Out of EdgeML

Electronic Engineering Journal 14 July 2020

Startup Takes a Dose of its Own Medicine

Read the full article at: https://www.eejournal.com/article/qeexo-takes-misery-out-of-edgeml/