ML Platform

From Data to Actionable Insights

Billions of sensors are collecting data on every device imaginable. Depending on whom you ask, up to 95.5% of the data collected could be left unused. Lacking sufficient machine learning expertise and resources, companies often do not know how to leverage this data. The Qeexo AutoML platform addresses these challenges, giving companies easy access to high-performance, lightweight machine learning models.

Challenges & Solutions

Challenge 1: Severe Shortage of Skilled Resources

Solved with Qeexo AutoML

“TinyML” (lightweight, embedded machine learning on MCUs) application development using sensor data traditionally requires deep expertise in machine learning, signal processing, data processing, optimization, and embedded engineering. There is virtually no engineer with all of these skillsets combined and it is difficult for big and small companies alike to build such a cross-functional team.

 

Qeexo AutoML replaces the need for these disparate experts, allowing existing resources to accomplish more, maximizing the efficiency of data science teams, freeing them from work they hate, and deploying them to areas they are needed most.

Challenge 2: Machine Learning is Labor-Intensive and Time-Consuming

Solved with Qeexo AutoML

Due to inefficient, fragmented tools available and the trial-and-error nature of machine learning, even experts take a long time to optimize and iterate through machine learning models that need to function in the field.

 

Qeexo AutoML’s end-to-end workflow dramatically reduces the time and effort required to aggregate and clean data, build and iterate through models, and deploy models onto embedded hardware. By automating many of the most tedious steps and putting in guardrails, Qeexo AutoML eliminates human-induced errors and significantly decreases the time to build machine learning solutions.

Challenge 3: Highly-Constrained Environments Are Difficult to Work With

Solved with Qeexo AutoML

Machine learning at the Edge, or TinyML, is subject to highly-constrained environments in terms of processing power, memory size, and battery life. Developing models optimized to run under these conditions is incredibly challenging.

 

 

Qeexo has spent years developing machine learning solutions for constrained environments using sensor data. Developed with our proprietary technology, Qeexo AutoML platform allows anyone to leverage our experience and expertise to build machine learning models to deliver high-performance at the Edge, with ultra-low latency, memory requirement, and power consumption.

Qeexo AutoML Workflow

Unlike other fragmented machine learning tools and frameworks that require expert engineers to cobble together, Qeexo AutoML walks users through the entire machine learning development process, all from within our intuitive UI – no coding necessary!

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Define Project e.g. Classification

Chose what type of machine learning best fits the problem at hand.

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Select Sensors and Target Hardware

Select a supported hardware module to see its sensors and their capabilities. Handpick the most relevant sensors or select them all and let Qeexo AutoML do the vetting.

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Collect/Upload Data

Intuitive UI is provided for users to easily collect, upload, and visualize sensor data.

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Automated Machine Learning

A myriad of configurations and knobs can be tweaked during the model-building process, using a spectrum of machine learning algorithms, to arrive at multiple machine learning models for comparison.

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Deploy/Download ML Package

Select the model that best fits your needs and deploy it onto the target embedded device with just one click.

Supported Hardware

ST SensorTile.box

Arduino nano 33 BLE Sense

STWIN Wireless Industrial Node (STEVAL-STWINKT1)

Cypress PSoC 6 WiFi-BT Pioneer Kit (CY8CKIT-062)

Try Qeexo AutoML

Register for a free evaluation or other SaaS options.

Try Qeexo AutoML

Register for a free evaluation or other SaaS options.