EmbeddedMachine Learning

IoT generates more and more data, available anywhere, anytime offering the ability to take relevant decisions. With Embedded Machine Learning, analyse Small Data on the spot. 

Move ML intelligence closer to where
it is really needed

Embedded models will predict and classify in a matter of milliseconds, to accelerate accurate and effective decisions.

Machine Learning and embedded solutions : a response to the coming Data Tsunami

In a few years, there will be more than 20 billions connected objects, deployed in the world, hosting billions of micro-controllers and sensors. 

All the Small Data generated can’t be sent to the cloud and is usually discarded for cost, bandwidth, storage, or power constraints.

Small Data

Embedded ML works on any infrastructure, from the cloud to one premises. In other words, where Small Data is produced. It can be deployed either on the company’s IT infrastructure or integrated with the company’s embedded software.

Embedded solutions lack awareness

Embedded specialists often have a strong perception that running ML on MCU is NOT possible. The main reason resides in the fact that sensors or micro-controllers have specific architectures and run on specialised CPU models.

small Datasets

Embedding Machine Learning capabilities in as little memory space as possible on a device, gives a second life to existing components. It provides added value to existing HW and increases the lifetime of such components. 

Embedded solutions enable any object from cars to coffee machines to be smarter

Embedded solutions for Small Data enhance hardware with prediction abilities. This opens up a wide range of new services and capabilities. The versatility of embedded solutions allows professionals to have full control on the deployment of the models, while engineering teams can work independently and more efficiently.

Small Data

MyDataModels embedded AI consumes very little CPU, memory and battery to decide if the data must be sent or not, leading to high performance competitive models with low memory footprint and minimal energy consumption

Machine Learning Models that Run Anywhere

When traditional Machine Learning requires data from different sources to be gathered and processed in a central location, embedded autoML is able to predict and classify in a matter of milliseconds. It runs where it is needed most, where data is produced and the decisions necessary i.e. at the edge of networks.

Start making sense of  your data

Test easily TADA with our test data here

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