Unlike most general purpose AI frameworks, SensiML Analytics Toolkit includes a client application to make the process of collecting, labeling, and cleansing supervised ML datasets vastly easier, manageable, and scalable from most methods currently in use by developers. In fact, most developers faced with dataset creation and management resort to their own means as the state of production quality tools for this task are pretty limited for AI algorithm development.
SensiML Data Capture Lab (DCL) provides a means to collect data directly from IoT sensor evaluation boards and prototype devices either through real-time streaming of raw sensor data or using offline storage to flash with subsequent batch mode retrieval. Data Capture Lab also supports bulk importing of existing sensor data using CSV text files.
Once collected within a SensiML Data Capture Lab project file, the DCL application provides version managed labeling of datasets by one or more users for a truly scalable and managed process of treating valuable sensor data intellectual property in a methodical workflow. Given labeled ML datasets can be as important a source of valuable IP as code which developers are accustomed to retaining in VCS tools, it makes sense to apply similar methodology to labeled ML data as SensiML Data Capture Lab uniquely enables.