If you are developing a smart IoT device or embedded application that involves sensors and sensor data processing, it is very likely that you can benefit from the SensiML Analytics Toolkit. The graphic below provides just a few examples of use cases where SensiML can be utilized to build models that transform raw sensor signals into useful application insight:
As an AutoML tool, SensiML can be utilized by a broad range of IoT device developers interacting at different levels with the toolkit. Data scientists and those with strong ML/AI skills can use the tool as a productivity aid to rapidly search and optimize for efficient model implementation for resource-constrained edge devices using a familiar Python client front-end. Domain experts, embedded developers, and hardware engineers who may be less skilled in machine learning and AI can utilize the UI driven constraints approach to define parameters and input datasets for the AutoML processing of potential algorithms to try. Academic researchers, startups, makers, or established product development teams can effectively use SensiML to quickly generate working sensor code that can learn from new projects or existing labeled datasets.
