{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T21:54:31Z","timestamp":1792965271045,"version":"4.5.0"},"reference-count":6,"publisher":"Association for Computing Machinery (ACM)","issue":"13","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2016,9]]},"abstract":"<jats:p>Machine Learning (ML) has become a mature technology that is being applied to a wide range of business problems such as web search, online advertising, product recommendations, object recognition, and so on. As a result, it has become imperative for researchers and practitioners to have a fundamental understanding of ML concepts and practical knowledge of end-to-end modeling. This tutorial takes a hands-on approach to introducing the audience to machine learning. The first part of the tutorial gives a broad overview and discusses some of the key concepts within machine learning. The second part of the tutorial takes the audience through the end-to-end modeling pipeline for a real-world income prediction problem.<\/jats:p>","DOI":"10.14778\/3007263.3007318","type":"journal-article","created":{"date-parts":[[2016,11,1]],"date-time":"2016-11-01T13:47:47Z","timestamp":1478008067000},"page":"1597-1600","source":"Crossref","is-referenced-by-count":16,"title":["Machine learning in the real world"],"prefix":"10.14778","volume":"9","author":[{"given":"Vineet","family":"Chaoji","sequence":"first","affiliation":[{"name":"Machine Learning, Amazon.com, Bangalore, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rajeev","family":"Rastogi","sequence":"additional","affiliation":[{"name":"Machine Learning, Amazon.com, Bangalore, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gourav","family":"Roy","sequence":"additional","affiliation":[{"name":"Machine Learning, Amazon.com, Bangalore, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2016,9]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"https:\/\/amplab.cs.berkeley.edu\/ml-pipelines\/","author":"Pipelines ML","year":"2014","unstructured":"ML Pipelines . https:\/\/amplab.cs.berkeley.edu\/ml-pipelines\/ , 2014 . {Online; accessed 17-July-2016}. ML Pipelines. https:\/\/amplab.cs.berkeley.edu\/ml-pipelines\/, 2014. {Online; accessed 17-July-2016}."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/2207809"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.5555\/1162264"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-21606-5"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.5555\/541177"},{"key":"e_1_2_1_6_1","volume-title":"Machine Learning: A Probabilistic Perspective","author":"Murphy K. P.","year":"2012","unstructured":"K. P. Murphy . Machine Learning: A Probabilistic Perspective . The MIT Press , 2012 . K. P. Murphy. Machine Learning: A Probabilistic Perspective. The MIT Press, 2012."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3007263.3007318","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T09:35:28Z","timestamp":1672220128000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3007263.3007318"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,9]]},"references-count":6,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2016,9]]}},"alternative-id":["10.14778\/3007263.3007318"],"URL":"https:\/\/doi.org\/10.14778\/3007263.3007318","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2016,9]]}}}