{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/rafiki-machine-learning-as-an-analytics","title":"Rafiki: Machine Learning as an Analytics Service System","arxiv_id":"1804.06087","date":"2018-04-17","proceeding":"PVLDB (The Proceedings of the VLDB Endowment) 2018 10","authors":["Wei Wang","Sheng Wang","Jinyang Gao","Meihui Zhang","Gang Chen","Teck Khim Ng","Beng Chin Ooi"],"abstract":"Big data analytics is gaining massive momentum in the last few years.\nApplying machine learning models to big data has become an implicit requirement\nor an expectation for most analysis tasks, especially on high-stakes\napplications.Typical applications include sentiment analysis against reviews\nfor analyzing on-line products, image classification in food logging\napplications for monitoring user's daily intake and stock movement prediction.\nExtending traditional database systems to support the above analysis is\nintriguing but challenging. First, it is almost impossible to implement all\nmachine learning models in the database engines. Second, expertise knowledge is\nrequired to optimize the training and inference procedures in terms of\nefficiency and effectiveness, which imposes heavy burden on the system users.\nIn this paper, we develop and present a system, called Rafiki, to provide the\ntraining and inference service of machine learning models, and facilitate\ncomplex analytics on top of cloud platforms. Rafiki provides distributed\nhyper-parameter tuning for the training service, and online ensemble modeling\nfor the inference service which trades off between latency and accuracy.\nExperimental results confirm the efficiency, effectiveness, scalability and\nusability of Rafiki.","url_abs":"http://arxiv.org/abs/1804.06087v1","url_pdf":"http://arxiv.org/pdf/1804.06087v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"rafiki-machine-learning-as-an-analytics","repo_url":"https://github.com/nginyc/rafiki","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"automl","task_name":"AutoML"},{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"hyperparameter-optimization","task_name":"Hyperparameter Optimization"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.06087","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.06087"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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