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What's the smallest machine learning model you've built? Qeexo's is so small, it can even run on an Arm Cortex M0+! Experience how easy it is to automate "tinyML" machine learning development for sensor modules – without having to write a single line of code!
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In this workshop-style Tech Talk, Qeexo:
Provides an overview of the benefits and challenges of running machine learning at the edge.

Walks attendees through the installation and setup of Qeexo AutoML. Please come with a Windows or Mac laptop with the Google Chrome browser installed.

Demos the simple data collection and visualization with our Qeexo AutoML interface for attendees to follow along to build their own classifiers.

Shows how to build multiple machine learning models and deploy them to an Arm Cortex-M4-powered sensor module for live-testing with just a few clicks.

Speakers: Tina Shyuan, Director of Product Marketing at Qeexo

Check out these top 4 questions asked during the talk:
1. What's Qeexo's engagement model for OEMs and enterprises?
For enterprise customers, Qeexo provides both custom Qeexo AutoML platform to use as internal ML engineering tool and a machine learning consulting service, which includes ML solution development and deployment.

2. I was using Qeexo with STWIN for a prototype project. The inference was working fine as I could see the classification results on the Qeexo window, however could please provide a brief idea on how the library can be implement so that the results can be display on a uart terminal or on tft screen connected to the board ?
Qeexo's free evaluation tier allows user to download the library file in .bin format for the Arduino Nano 33 BLE Sense, so that user can merge the classifier with their master firmware. Library download for STWIN device is only available for the Pro (paid) tier users. If interested, please contact Qeexo for an upgrade to Pro.

3. What ML algorithms are available? How can I edit/modify the algorithms?
Qeexo currently supports 17 different machine learning algorithms, including: GBM, XGBoost, Random Forest, Logistic Regression, Gaussian Naive Bayes, Decision Tree, Polynomial SVM, RBF SVM, SVM, CNN, RNN, CRNN, and ANN for multi-class classification; and Local Outlier Factor, One Class SVM, One Class Random Forest, and Isolation Forest for single-class classification. For each algorithm, you can set its configurations and parameters where available, in Qeexo's UI when building the models, including options such as quantization. Qeexo are also working on adding more flexibility into their Qeexo AutoML platform.

4. Are the underlying computations floating point or int?
Qeexo support both floating point and fixed-point architectures. For the Cortex M4 MCUs with floating point support, Qeexo take advantage of the additional precision; for the Cortex M0 platforms, Qeexo can also operate with fixed-point.

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