{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T08:27:13Z","timestamp":1771835233598,"version":"3.50.1"},"reference-count":0,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2019,8,1]],"date-time":"2019-08-01T00:00:00Z","timestamp":1564617600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Queue"],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>Breck et al. share details of the pipelines used at Google to validate petabytes of production data every day. With so many moving parts it\u2019s important to be able to detect and investigate changes in data distributions before they can impact model performance. \"Software Engineering for Machine Learning: A Case Study\" shares lessons learned at Microsoft as machine learning started to pervade more and more of the company\u2019s systems, moving from specialized machine-learning products to simply being an integral part of many products and services.<\/jats:p>","DOI":"10.1145\/3358955.3365847","type":"journal-article","created":{"date-parts":[[2020,9,3]],"date-time":"2020-09-03T04:10:13Z","timestamp":1599106213000},"page":"17-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Putting Machine Learning into Production Systems"],"prefix":"10.1145","volume":"17","author":[{"given":"Adrian","family":"Colyer","sequence":"first","affiliation":[{"name":"Accel"}]}],"member":"320","published-online":{"date-parts":[[2019,8]]},"container-title":["Queue"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3358955.3365847","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3358955.3365847","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:13:26Z","timestamp":1750202006000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3358955.3365847"}},"subtitle":["Data validation and software engineering for machine learning"],"short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2019,8]]}},"alternative-id":["10.1145\/3358955.3365847"],"URL":"https:\/\/doi.org\/10.1145\/3358955.3365847","relation":{},"ISSN":["1542-7730","1542-7749"],"issn-type":[{"value":"1542-7730","type":"print"},{"value":"1542-7749","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,8]]},"assertion":[{"value":"2019-08-01","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}