  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.
