Migrating a recommendation system to cloud using ML workflow

Abstract

Inference is the production stage of machine learning workflow in which a trained model is used to infer or predict with real world data. A recommendation system improves customer experience by displaying most relevant items based on historical behavior of a customer. Machine learning models built for recommendation systems are deployed either on-premise or migrated to a cloud for inference in real time or batch. A recommendation system should be cost effective while honoring service level agreements (SLAs). In this work we discuss on-premise implementation of our recommendation system called iPrescribe. We show a methodology to migrate on-premise implementation of recommendation system to a cloud using ML workflow. We also present our study on performance of recommendation system model when deployed on different types of virtual instances.