In this presentation I do a review of the architecture of an AI application for IoT environments.
Since specific modeling and training aspects also have an impact on the final implementation of an enterprise ready solution, such solutions become very complex pretty soon.
The complexity of AI system for IoT is a big challenge – thus, I want to break this complexity down into particular views, which emphasize the individual but still interconnected aspects more clearly.
Event Driven:
Press a button (Amazon, Feedback button in Airport)
Order something
Message Driven:
A person appears at a place (in a shop), gets recognized, and triggers an action (offering)
Person speaks to robot
Data Driven
Self driving car: data represents the environment in the digital world
Chat bot: messages represent the customer’s intention
Anomalie / fraud detection scenarios: variation and unexpected deviations in data are indicators