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Until now building machine learning (ML) algorithms for hardware meant complex mathematical mode s based on sample data, known as “ training data ,” in order to make predictions or decisions without being explicitly programmed to do so.

And if this sounds complex and expensive to build, it is. 

But, what if there’s another, more agile way.

The implications of TinyML accessibility are very important in today’s world. For example, a typical drug development trial takes about five years as there are potentially millions of design decisions that need to be made on route to FDA approval. Using the power of TinyML and hardware, not animals, for testing models can speed up the process and take just 12 months.

Another example of this game-changing technology in terms of building neural networks is the ability to fix problems and create new solutions for things we couldn’t dream of doing before. For example, TinyML can listen to beehives and detect anomalies and distress caused by things as small as wasps. A tiny sensor can trigger an alert based on a sound model that identifies a hive under attack, allowing farmers to secure and assist the hive, in real-time.

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