Intelligence IoT Edge is playing a critical role in services that require real time inferencing. Historically, there have been systems with a high amount of engineering complexity in terms of deployment and also in operation. For example, SCADA is one such system that has been working in the Power Generation industry, Oil and Gas industry, Cement factories etc. In fact, SCADA includes humans in a loop and makes it as Supervisory control and Data acquisition.

In the advent of Deep learning and its success in the modern Digital side, there have been huge amounts of interest among researchers to carry Deep learning Models to above mentioned industrial verticals and trying to bring up Intelligent control and Data acquisition. In the place of Supervisor, it appears that an intelligent IoT edge is coming up to perform those tasks that are handled by Human beings in the form of Supervisor. Thus there is immense interest in making IoT Edge as intelligent systems in these core engineering verticals apart from consumer industry requirements.

DLtrain designed to include NN models, Train a NN model with training data ( mostly use MNIST ) and validate trained NN models before going for deployment in IoT Edge. In the case of deployment, there is a huge interest in making Smart Phones as IoT Edge such that the same device can be used without much investment during the learning time of each learner. However, industrial deployment is expected to happen in devices like Jetson Nano, Ultra96-V2, mmWave Radar IWR 6843 etc. Maybe, as an advanced effort, there is a plan to cover the above mentioned IoT Edges.