IoT Edge Inferencing and Learning
Whether your needs require edge algorithm execution or edge adaptive learning models, SensiML Knowledge Packs can accommodate your application.
For applications where a defined edge algorithm meets the need, Knowledge Packs can be easily constructed and tested in the cloud and deployed to the edge device. Developers may implement any mechanism to associate or update a device with a given Knowledge Pack based on contextual information or user preference for that device. Model selection can utilize universal models or be more narrowly defined based on metadata that links to Knowledge Packs trained on specific subsets of data.
For those applications that demand true per-instance customization, this can also be achieved using adaptive edge model tuning. This more advanced feature is an available option for users to implement personalized model parameters from true on-device tuning using the SensiML API to adjust model weights and parameters locally.