Keywords:Prediction Model, Business Deplolyment, Framework, Demand Forecasting, Failure Prediction
Recently, many predictive models of machine learning algorithms have been deployed in real business works. In such a situation, issues from the deployment of machine learning models are discussed at length under 'ML Ops'. We discuss that an important practice of ML Ops cannot be applied to models used in the retail stores where the results of prediction are often achieved after a certain period. In this paper, we propose a framework for such a condition of delayed prediction results, to stimulate further discussion. With this framework, we conclude that one should monitor the data drift and business operators should play a role beyond a user.
Authentication for paper PDF access
A password is required to view paper PDFs. If you are a registered participant, please log on the site from Participant Log In.
You could view the PDF with entering the PDF viewing password bellow.