Semi-Automating Research-to-Operation of AI Models with Python

Having a simplified process to bring research models to operation (R2O) is highly beneficial but a challenge given the constant changes in both technologies and modeling approaches. Operationalizing the models helps deploy and communicate predictions to stakeholders and the public generating exposure and feedback. A rapid transition to at least the prototype or local stage is critical to obtain this feedback and be able to more realistically co-produce operational models with future users. Semaphore is a python program that semi-automates the operationalizing AI models, at present models that can be saved as .H5 files. Semaphore uses an agile design, specific data objects, an easily expandable data ingestion module, and a database to support a wide variety of data series. It uses a combination of .H5 files and Semaphore-specific data specification files to be able to interact with AI models regardless of data or design. Data is standardized to a unique set of keys that allow Semaphore to understand any data series. This helps series information to be easily communicated, saved, and queried. Semaphore will ease the burden of operationalizing a model from researchers, allowing the process to be quicker. Semaphore also offers the option to store model predictions along with the operationalized model's input data sets in a single easily accessible and easily workable place.