Rafiki: Machine Learning as an Analytics Service System

By Wei Wang, Sheng Wang, Jinyang Gao, Meihui Zhang, Gang Chen, Teck Khim Ng, Beng Chin Ooi. 2018

127 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Radwa El Shawi
2. Sherif Sakr
3. Mohamed Maher
4. Feng Yan
5. Chengliang Zhang
6. Wei Wang
7. Minchen Yu
8. Marco F. Huber

In Computer Vision and Pattern Recognition

1. Feng Yan
2. Sherif Sakr
3. Radwa El Shawi
4. Mohamed Maher
5. Chengliang Zhang
6. Wei Wang
7. Minchen Yu

Citations in the corpus

• Automated Machine Learning: State-of-The-Art and Open Challenges (2019, methodology): “Rafiki 21 has been introduced as a distributed framework which is based on the idea of using previous models that achieved high performance on the same tasks [126].”
• MArk: Exploiting Cloud Services for Cost-Effective, SLO-Aware Machine Learning Inference Serving (2019, methodology): “For example, systems like Clipper [30], Rafiki [72], and MXNet Model Server [24] host each model in a separate Docker [4] container to ensure process isolation; TensorFlow Serving [55] deploy models as servables, which are executed as black box containers and can also be used for version management.”