Machine learning is commonly associated with big data, but that’s changing quickly. While IoT, edge computing and intelligent edge devices arguably make big data even bigger, not all data at the edge is useful. Therefore, the data needs to be analyzed at the edge to separate the signal from the noise. Other types of pattern recognition are also occurring at the edge, such as the discovery of patterns in data depicting the impact of weather events on crops or people at risk of heart attacks. Determining the status of something in the field requires machine learning. Yet, IoT devices tend to be low-power devices. Enter TinyML and endless use cases for edge analytics become possible.
Read more : Why TinyML use cases are taking off.
