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Deep learning model inference is a key service in many businesses and scientific discovery processes. This paper introduces Ribbon, a novel deep learning inference serving system that meets two competing objectives: quality-of-service (QoS) target and cost-effectiveness. The key idea behind Ribbon is to intelligently em-ploy a diverse set of cloud computing instances (heterogeneous instances) to meet the QoS target and maximize cost savings. Rib-bon devises a Bayesian Optimization-driven strategy that helps users build the optimal set of heterogeneous instances for their model inference service needs on cloud computing platforms - and, Ribbon demonstrates its superiority over existing approaches of inference serving systems using homogeneous instance pools. Ribbon saves up to 16&#x0025; of the inference service cost for different learning models including emerging deep learning recommender system models and drug-discovery enabling models.

RIBBON: Cost-Effective and QoS-Aware Deep Learning Model Inference using a Diverse Pool of Cloud Computing Instances