On Using Deep Learning for Business Analytics: At what cost?

With advances in AI (Artificial Intelligence) and the surge of Big Data, Deep learning (DL) has a remarkable impact on businesses in various domains including finance, marketing, healthcare, manufacturing, and agriculture. From sales prediction to detection and monitoring customer behaviors, its use in business analytics is proven to help grow and improve business functions. However, execution of DL can instigate high cost during both model training and deployment. Time and energy are two essential cost factors of DL computation as they imply not only monetary but also environmental consequences. Moreover, different DL architecture incurs different costs. Selecting the best model to train and deploy is hence important to the business. Most existing work focuses on building a DL model that obtains the highest accuracy or similar measures to evaluate quality of data analytics. In this paper, we argue that in practice, such performance criteria may not be adequate, for example, the most accurate model may not be the most cost-effective. This paper studies a practical approach that includes energy consumption during training and deploying the trained DL in deciding what DL model should be deployed. The paper describes how energy consumption can be estimated and illustrates the approach for cost analysis of DL application on the smart agriculture business where plant disease classification model is trained and deployed. Lessons learned from the study is included. In summary, our contributions are: (1) an approach to analyze cost of using DL model in terms of time, accuracy and energy consumption to support model selection, and (2) application of the approach on plant disease classification in smart agriculture business.