{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:10:42Z","timestamp":1750219842145,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":13,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T00:00:00Z","timestamp":1683504000000},"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":[],"published-print":{"date-parts":[[2023,5,8]]},"DOI":"10.1145\/3578356.3592581","type":"proceedings-article","created":{"date-parts":[[2023,5,4]],"date-time":"2023-05-04T19:44:37Z","timestamp":1683229477000},"page":"1-7","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Actionable Data Insights for Machine Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8245-128X","authenticated-orcid":false,"given":"Ming-Chuan","family":"Wu","sequence":"first","affiliation":[{"name":"Apple, Seattle, United States of America"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1464-8610","authenticated-orcid":false,"given":"Manuel","family":"B\u00e4hr","sequence":"additional","affiliation":[{"name":"Apple, Heidelberg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6969-5635","authenticated-orcid":false,"given":"Nils","family":"Braun","sequence":"additional","affiliation":[{"name":"Apple, Heidelberg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4671-0707","authenticated-orcid":false,"given":"Katrin","family":"Honauer","sequence":"additional","affiliation":[{"name":"Apple, Heidelberg, Germany"}]}],"member":"320","published-online":{"date-parts":[[2023,5,8]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Python in Science Conference (SCIPY)","author":"Bantilan Niels","year":"2020","unstructured":"Niels Bantilan . 2020 . pandera: Statistical Data Validation of Pandas Dataframes . Python in Science Conference (SCIPY) (2020). Niels Bantilan. 2020. pandera: Statistical Data Validation of Pandas Dataframes. Python in Science Conference (SCIPY) (2020)."},{"key":"e_1_3_2_1_2_1","volume-title":"Proceedings of the 2nd SysML Conference","author":"Breck E.","year":"2019","unstructured":"E. Breck , M. Zinkevich , N. Polyzotis , S. Whang , and S. Roy . 2019. Data validation for machine learning . Proceedings of the 2nd SysML Conference ( 2019 ). E. Breck, M. Zinkevich, N. Polyzotis, S. Whang, and S. Roy. 2019. Data validation for machine learning. Proceedings of the 2nd SysML Conference (2019)."},{"key":"e_1_3_2_1_3_1","volume-title":"Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems","author":"B\u00e4uerle Alex","year":"2022","unstructured":"Alex B\u00e4uerle , \u00c1ngel Alexander Cabrera , Fred Hohman , Megan Maher , David Koski , Xavier Suau , Titus Barik , and Dominik Moritz . 2022 . Symphony: Composing Interactive Interfaces for Machine Learning . Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (2022). Alex B\u00e4uerle, \u00c1ngel Alexander Cabrera, Fred Hohman, Megan Maher, David Koski, Xavier Suau, Titus Barik, and Dominik Moritz. 2022. Symphony: Composing Interactive Interfaces for Machine Learning. Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (2022)."},{"unstructured":"DeepLearningAI. 2021; accessed April 2021. A Chat with Andrew on MLOps: From Model-centric to Data-centric AI. https:\/\/www.youtube.com\/watch?v=06-AZXmwHjo.  DeepLearningAI. 2021; accessed April 2021. A Chat with Andrew on MLOps: From Model-centric to Data-centric AI. https:\/\/www.youtube.com\/watch?v=06-AZXmwHjo.","key":"e_1_3_2_1_4_1"},{"key":"e_1_3_2_1_5_1","volume-title":"Hanna M. Wallach, Hal Daum\u00e9 III, and Kate Crawford.","author":"Gebru Timnit","year":"2021","unstructured":"Timnit Gebru , Jamie Morgenstern , Briana Vecchione , Jennifer Wortman Vaughan , Hanna M. Wallach, Hal Daum\u00e9 III, and Kate Crawford. 2021 . Datasheets for datasets. Communications of ACM 64, 12 (2021). Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna M. Wallach, Hal Daum\u00e9 III, and Kate Crawford. 2021. Datasheets for datasets. Communications of ACM 64, 12 (2021)."},{"volume-title":"Building Machine Learning Pipelines","author":"Hapke Hannes","unstructured":"Hannes Hapke and Catherine Nelson . 2020. Building Machine Learning Pipelines , Chapter 4: Data Validation. O'Reilly Media, Inc. Hannes Hapke and Catherine Nelson. 2020. Building Machine Learning Pipelines, Chapter 4: Data Validation. O'Reilly Media, Inc.","key":"e_1_3_2_1_6_1"},{"key":"e_1_3_2_1_7_1","volume-title":"Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems","author":"Hohman Fred","year":"2020","unstructured":"Fred Hohman , Kanit Wongsuphasawat , Mary Beth Kery , and Kayur Patel . 2020 . Understanding and Visualizing Data Iteration in Machine Learning . Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (2020). Fred Hohman, Kanit Wongsuphasawat, Mary Beth Kery, and Kayur Patel. 2020. Understanding and Visualizing Data Iteration in Machine Learning. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (2020)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1038\/s42256-022-00516-1","article-title":"Advances, challenges and opportunities in creating data for trustworthy AI","volume":"4","author":"Liang Weixin","year":"2022","unstructured":"Weixin Liang , Girmaw Abebe Tadesse , Daniel Ho , L. Fei-Fei , Matei Zaharia , Ce Zhang , and James Zou . 2022 . Advances, challenges and opportunities in creating data for trustworthy AI . Nature Machine Intelligence 4 , 8 (2022), 669 -- 677 . Weixin Liang, Girmaw Abebe Tadesse, Daniel Ho, L. Fei-Fei, Matei Zaharia, Ce Zhang, and James Zou. 2022. Advances, challenges and opportunities in creating data for trustworthy AI. Nature Machine Intelligence 4, 8 (2022), 669--677.","journal-title":"Nature Machine Intelligence"},{"key":"e_1_3_2_1_9_1","article-title":"An Empirical Study of the Impact of Hyperparameter Tuning and Model Optimization on the Performance Properties of Deep Neural Networks","volume":"31","author":"Liao Lizhi","year":"2022","unstructured":"Lizhi Liao , Heng Li , Weiyi Shang , and Lei Ma . 2022 . An Empirical Study of the Impact of Hyperparameter Tuning and Model Optimization on the Performance Properties of Deep Neural Networks . ACM Transactions on Software Engineering and Methodology 31 , 3 (2022). Lizhi Liao, Heng Li, Weiyi Shang, and Lei Ma. 2022. An Empirical Study of the Impact of Hyperparameter Tuning and Model Optimization on the Performance Properties of Deep Neural Networks. ACM Transactions on Software Engineering and Methodology 31, 3 (2022).","journal-title":"ACM Transactions on Software Engineering and Methodology"},{"key":"e_1_3_2_1_10_1","volume-title":"Communications of ACM 65, 3","author":"Monroe Don","year":"2022","unstructured":"Don Monroe . 2022. Accelerating AI. Communications of ACM 65, 3 ( 2022 ). Don Monroe. 2022. Accelerating AI. Communications of ACM 65, 3 (2022)."},{"key":"e_1_3_2_1_11_1","volume-title":"Proceedings of the 14th python in science conference","author":"Rocklin M.","year":"2015","unstructured":"M. Rocklin . 2015 . Dask: Parallel computation with blocked algorithms and task scheduling . Proceedings of the 14th python in science conference (2015). M. Rocklin. 2015. Dask: Parallel computation with blocked algorithms and task scheduling. Proceedings of the 14th python in science conference (2015)."},{"key":"e_1_3_2_1_12_1","volume-title":"Conference of European Statistics, Workshop on Statistical Data Editing","author":"Ruiz Christian","year":"2018","unstructured":"Christian Ruiz . 2018 . Improving Data Validation using Machine Learning . Conference of European Statistics, Workshop on Statistical Data Editing (2018). Christian Ruiz. 2018. Improving Data Validation using Machine Learning. Conference of European Statistics, Workshop on Statistical Data Editing (2018)."},{"key":"e_1_3_2_1_13_1","volume-title":"Proceedings of the VLDB endowment 11","author":"Schelter S.","year":"2018","unstructured":"S. Schelter , D. Lange , P. Schmidt , M. Celikel , F. Viessmann , and A. Grafberger . 2018. \"Automating Large-Scale Data Quality Verification \". Proceedings of the VLDB endowment 11 , 12 ( 2018 ). S. Schelter, D. Lange, P. Schmidt, M. Celikel, F. Viessmann, and A. Grafberger. 2018. \"Automating Large-Scale Data Quality Verification\". Proceedings of the VLDB endowment 11, 12 (2018)."}],"event":{"sponsor":["SIGOPS ACM Special Interest Group on Operating Systems"],"acronym":"EuroMLSys '23","name":"EuroMLSys '23: 3rd Workshop on Machine Learning and Systems","location":"Rome Italy"},"container-title":["Proceedings of the 3rd Workshop on Machine Learning and Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3578356.3592581","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:46:51Z","timestamp":1750178811000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3578356.3592581"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,8]]},"references-count":13,"alternative-id":["10.1145\/3578356.3592581","10.1145\/3578356"],"URL":"https:\/\/doi.org\/10.1145\/3578356.3592581","relation":{},"subject":[],"published":{"date-parts":[[2023,5,8]]},"assertion":[{"value":"2023-05-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}