Working together, Lityx and Conservation Labs used sound frequency data collected from H2know sensors to build initial water flow prediction models and test them in-market. LityxIQ includes automated machine learning and deployment capabilities that made testing a wide variety of algorithms and approaches easy and efficient. The platform also provides comprehensive data enhancement functionality, allowing hundreds of features to be computed from the raw data and serve as machine learning inputs.
Putting these models into a production environment was easy, using LityxIQ. No additional effort is required to score new datasets (collected ongoing from H2know IoT sensors), and connectivity to live IoT data from the Conservations Labs data backend was easily set up.
An automated process was created to import data on a near-real-time basis and process it for analytics, clustering, and water flow prediction. The Conservation Labs team has created user dashboards for viewing and analyzing those outputs once results have been automatically pushed back to their IT environment.