Machine learning in embedded systems allows predictions in automated business processes to make more educated predictions. Running machine learning models on embedded devices is called embedded machine learning. Machine learning provides tons of historical data and enables electronic systems to learn autonomously and use the insight for predictions, decision making and analysis.
Benefits of embedded devices in machine learning
There are many benefits to using machine learning in embedded devices.
- It minimizes the risk of privacy leaks and data breaches by eliminating the use of cloud servers to store and transfer data.
- It economizes the network resources and bandwidth by removing cloud storage.
- The microcontrollers in the device are power efficient thus a very lower carbon footprint.
- Embedded systems have significant network latency than cloud-based systems due to the elimination of transfer of tons of data to the cloud in the embedded systems.
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