Deploying A Machine Learning Solution As A Surrogate

Chuanhe Jay Shan, Ahmed Wahba, Li-C. Wang, Nik Sumikawa

Abstract: A machine learning (ML) solution can be non-robust and when it is deployed, can make mistakes on the future unseen data. Consequently, deployment of a ML solution might demand continuous service from its ML developer. Using wafer image classification as an example, this paper presents the design of a ML solution where its deployment is facilitated by the continuous service from its ML expert.