A Recommendation System for Cloud AI Services

Many learned inference engines have been released as cloud-based AI services. However, learned AI services are black boxes, and it is difficult for users to decide which service to choose. We propose a comparison service to infer the best AI service by learning their different output results as training data. Our model, “AI for selecting the best AI,” involves meta-learning; it learns the output of the cloud-based AI service as metadata. We compared and evaluated the accuracy and cost of two proposed models that recommend the best among several commercial AI services and an ensemble method. The results of our experiments to infer face attributes (i.e., age and gender) on a face image dataset crawled from Wikipedia showed that the accuracy of our system was higher than that of single face classification cloud-based AI service. Notably, results on inferring age and gender, where training data for each service showed a significant difference in the tendency for accuracy, had 6.2% higher accuracy compared to existing cloud-based AIs.