Improving Algorithm Performance: Managing the Full Machine Learning Model Lifecycle

Abstract

Machine learning (ML) has transformed science and industry by uncovering concealed patterns and generating data-driven forecasts. The success of an ML model necessitates intricate design and ongoing administration throughout its lifecycle. The model operates with interrelated stages of life. This study systematically examines the lifecycle management of machine learning models from inception to deployment and operationalization.