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