Rafiki: Machine Learning as an Analytics Service System
This system addresses the problem of requiring expertise to optimize machine learning in big data applications, making analytics more accessible, though it is incremental as it builds on existing cloud and ML service concepts.
The paper tackles the challenge of integrating machine learning into big data analytics by developing Rafiki, a system that provides training and inference services on cloud platforms, with experimental results confirming its efficiency, effectiveness, scalability, and usability.
Big data analytics is gaining massive momentum in the last few years. Applying machine learning models to big data has become an implicit requirement or an expectation for most analysis tasks, especially on high-stakes applications.Typical applications include sentiment analysis against reviews for analyzing on-line products, image classification in food logging applications for monitoring user's daily intake and stock movement prediction. Extending traditional database systems to support the above analysis is intriguing but challenging. First, it is almost impossible to implement all machine learning models in the database engines. Second, expertise knowledge is required to optimize the training and inference procedures in terms of efficiency and effectiveness, which imposes heavy burden on the system users. In this paper, we develop and present a system, called Rafiki, to provide the training and inference service of machine learning models, and facilitate complex analytics on top of cloud platforms. Rafiki provides distributed hyper-parameter tuning for the training service, and online ensemble modeling for the inference service which trades off between latency and accuracy. Experimental results confirm the efficiency, effectiveness, scalability and usability of Rafiki.