IBM’s Castor Makes It Easy To Manage Infinite Data From IoT Devices

A time-series model needs frequent re-training to maintain the accuracy of the forecasts. For example, modelling weather data requires the data scientist to keep up with the pace of change in the environment to monitor the changes in a pattern which requires regular benchmarking of the predictive models. In contrast to AI-driven cases using a small number of big models for image processing or natural language, IBM’s Castor aims at the Internet of Things applications which need numerous smaller models. Every model is associated with an entity describing where the data originates; for example, “Company ABC”, and a signal describing what is measured, like “hourly revenue”.  This system allows users to retrieve data with a simple command like: getTimeseries(servername, “entity”, “measure”) The training of the models is done using Python or R. The models are stored separately from configuration and runtime parameters, which enables the user to change the details of the model without redeployment.