Imagimob announced that its new release of the tinyML platform Imagimob AI supports end-to-end development of deep learning anomaly detection. A big strength of deep learning anomaly detection is that it delivers high performance as well as eliminates the need for feature engineering, thus saving costs and reducing time-to-market.
Not only is deep learning anomaly detection better for eliminating the need for feature engineering but it can also leverage and deliver excellent performance on the new generation of powerful neural network processors that are now hitting the market. This means that when going to the edge customers can make the most of their hardware.
Feature engineering, in simple terms, is the act of converting raw observations into desired features using statistical or mathematical functions. Feature engineering normally requires domain expertise and is in general very time-consuming.
With the added support for autoencoder networks in Imagimob AI, developers can now build anomaly detection in less time, and with better performance. Customers will be able to reduce development costs and shorten the time to market.
The anomaly detection solution from Imagimob has been tested and verified on the real-world machine and sensor data.