What is the focus?
The goal of an Edge AI deployment is to make sense of this data, identifying patterns and using them to make decisions.
An embedded device running an Edge AI, application has a limited frame in which to collect this data, process it, feed it into some kind of AI algorithm, and act on the results.
The need to tame raw sensor data makes Digital signal processing a critical part of most Edge AI deployment.
In contrast, edge AI Tools are built to handle constant streams of sensor data.
In Edge AI, developers creating embedded machine learning applications have to balance the size of their model against the accuracy they require.
Learning from feedback is limited
Part of the magic of EdgeAI is that we can deploy intelligence to devices that have limited connectivity.
This presents a big challenge for our application development workflow. How do we make sure our system is performing well in the real world when we have limted acces to it?. And how we can we imporve our system when it’s so dificult to collect more data?. This is a core topic of edge AI develoment.
Summary
On the positive side, this means we can draw form a rich ecosystem of libaries and frameworks that is proven to work well. However, few of the existing tools prioritize things that are important on the edge – like small sizes, computational efficiency, and the ability to train on small amounts of data.
More?
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