{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,28]],"date-time":"2025-09-28T04:05:39Z","timestamp":1759032339683},"reference-count":14,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2018,8]]},"abstract":"<jats:p>\n            We demonstrate ease.ml, a multi-tenant machine learning service we host at ETH Zurich for various research groups. Unlike existing machine learning services, ease.ml presents a novel architecture that supports multi-tenant, cost-aware model selection that optimizes for minimizing total regrets of all users. Moreover, it provides a novel user interface that enables\n            <jats:italic>declarative<\/jats:italic>\n            machine learning at a higher level: Users only need to specify the input\/output schemata of their learning tasks and ease.ml can handle the rest. In this demonstration, we present the design principles of ease.ml, highlight the implementation of its key components, and showcase how ease.ml can help ease machine learning tasks that often perplex even experienced users.\n          <\/jats:p>","DOI":"10.14778\/3229863.3236258","type":"journal-article","created":{"date-parts":[[2018,9,10]],"date-time":"2018-09-10T12:12:28Z","timestamp":1536581548000},"page":"2054-2057","source":"Crossref","is-referenced-by-count":5,"title":["Ease.ml in action"],"prefix":"10.14778","volume":"11","author":[{"given":"Bojan","family":"Karla\u0161","sequence":"first","affiliation":[{"name":"ETH Zurich"}]},{"given":"Ji","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Rochester"}]},{"given":"Wentao","family":"Wu","sequence":"additional","affiliation":[{"name":"Microsoft Research"}]},{"given":"Ce","family":"Zhang","sequence":"additional","affiliation":[{"name":"ETH Zurich"}]}],"member":"320","published-online":{"date-parts":[[2018,8]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"2962","volume-title":"Proc. NIPS","author":"Feurer M.","year":"2015","unstructured":"M. Feurer , A. Klein , K. Eggensperger , J. Springenberg , M. Blum , and F. Hutter . Efficient and robust automated machine learning . In Proc. NIPS , pages 2962 -- 2970 , 2015 . M. Feurer, A. Klein, K. Eggensperger, J. Springenberg, M. Blum, and F. Hutter. Efficient and robust automated machine learning. In Proc. NIPS, pages 2962--2970, 2015."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098043"},{"key":"e_1_2_1_3_1","unstructured":"GPyOpt. GPyOpt: A bayesian optimization framework in python. http:\/\/github.com\/SheffieldML\/GPyOpt 2016.  GPyOpt. GPyOpt: A bayesian optimization framework in python. http:\/\/github.com\/SheffieldML\/GPyOpt 2016."},{"key":"e_1_2_1_4_1","first-page":"5","article-title":"Auto-weka 2.0: Automatic model selection and hyperparameter optimization in weka","volume":"18","author":"Kotthoff L.","year":"2017","unstructured":"L. Kotthoff , C. Thornton , H. H. Hoos , F. Hutter , K. Leyton-Brown , Auto-weka 2.0: Automatic model selection and hyperparameter optimization in weka . JMLR , 18 : 5 , 2017 . L. Kotthoff, C. Thornton, H. H. Hoos, F. Hutter, K. Leyton-Brown, et al. Auto-weka 2.0: Automatic model selection and hyperparameter optimization in weka. JMLR, 18:5, 2017.","journal-title":"JMLR"},{"key":"e_1_2_1_5_1","volume-title":"Hyperband: A novel bandit-based approach to hyperparameter optimization. arXiv preprint arXiv:1603.06560","author":"Li L.","year":"2016","unstructured":"L. Li , K. Jamieson , G. DeSalvo , A. Rostamizadeh , and A. Talwalkar . Hyperband: A novel bandit-based approach to hyperparameter optimization. arXiv preprint arXiv:1603.06560 , 2016 . L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, and A. Talwalkar. Hyperband: A novel bandit-based approach to hyperparameter optimization. arXiv preprint arXiv:1603.06560, 2016."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3187009.3177737"},{"issue":"1","key":"e_1_2_1_7_1","first-page":"18","article-title":"A review of automatic selection methods for machine learning algorithms and hyper-parameter values","volume":"5","author":"Luo G.","year":"2016","unstructured":"G. Luo . A review of automatic selection methods for machine learning algorithms and hyper-parameter values . NetMAHIB , 5 ( 1 ): 18 , 2016 . G. Luo. A review of automatic selection methods for machine learning algorithms and hyper-parameter values. NetMAHIB, 5(1):18, 2016.","journal-title":"NetMAHIB"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2017.192"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2015.2494218"},{"key":"e_1_2_1_10_1","first-page":"2951","volume-title":"NIPS","author":"Snoek J.","year":"2012","unstructured":"J. Snoek , H. Larochelle , and R. P. Adams . Practical bayesian optimization of machine learning algorithms . In NIPS , pages 2951 -- 2959 , 2012 . J. Snoek, H. Larochelle, and R. P. Adams. Practical bayesian optimization of machine learning algorithms. In NIPS, pages 2951--2959, 2012."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2806777.2806945"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2487629"},{"key":"e_1_2_1_13_1","volume-title":"Multi-device, multi-tenant model selection with gp-ei","author":"Yu C.","year":"2018","unstructured":"C. Yu , C. Zhang , J. Zhong , B. Karlas , and J. Liu . Multi-device, multi-tenant model selection with gp-ei . 2018 . under review. C. Yu, C. Zhang, J. Zhong, B. Karlas, and J. Liu. Multi-device, multi-tenant model selection with gp-ei. 2018. under review."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3077257.3077265"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3229863.3236258","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T10:07:40Z","timestamp":1672222060000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3229863.3236258"}},"subtitle":["towards multi-tenant declarative learning services"],"short-title":[],"issued":{"date-parts":[[2018,8]]},"references-count":14,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2018,8]]}},"alternative-id":["10.14778\/3229863.3236258"],"URL":"https:\/\/doi.org\/10.14778\/3229863.3236258","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2018,8]]}}}