The thesis aims at designing a prototype of a configurable Machine Learning pipeline for the healthcare domain, where risk prediction is provided as a RESTful web service. The pipeline should enable selection of the model to train, the desired performance metrics to achieve, the structure of input data compliant with the model application. Promising tools are Smile and SparkML.
keywords
machine learning, as a service, REST, healthcare, risk prediction
