{"id":28496,"date":"2022-02-17T06:00:00","date_gmt":"2022-02-17T14:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=28496"},"modified":"2023-06-23T12:43:30","modified_gmt":"2023-06-23T19:43:30","slug":"research-highlights-autodc-automated-data-centric-processing","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2022\/02\/17\/research-highlights-autodc-automated-data-centric-processing\/","title":{"rendered":"Research Highlights: AutoDC: Automated Data-centric Processing"},"content":{"rendered":"
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\"\"<\/figure><\/div>\n\n\n

Most AutoML solutions are developed with a model-centric approach, however, according to a research paper, “AutoDC Automated Data-centric Processing<\/a>,” that was accepted into last year\u2019s highly selective NeurIPS conference on the development of an automated data-centric tool (AutoDC), it was found to save an estimated 80% of the manual time needed for data set improvement \u2013 typically a bespoke and costly process.<\/p>\n\n\n\n

One of the authors, Zac Yung-Chun Liu, Chief Data Scientist at Hypergiant<\/a> and part-time research associate at Stanford,<\/a> is an expert in the field of AutoML. The paper offers a perspective for practical data-centric AutoML solutions, and how this new no-code and low-code solution is one of many R&D efforts that will push the field forward.<\/p>\n\n\n\n

Sign up for the free insideBIGDATA newsletter<\/a>.<\/em><\/p>\n\n\n\n

Join us on Twitter: @InsideBigData1 \u2013 https:\/\/twitter.com\/InsideBigData1<\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

Most AutoML solutions are developed with a model-centric approach, however, according to a research paper, “AutoDC Automated Data-centric Processing,” that was accepted into last year\u2019s highly selective NeurIPS conference on the development of\u00a0an automated data-centric tool (AutoDC), it was found to save an estimated 80% of the manual time needed for data set improvement\u00a0\u2013 typically a bespoke and costly process.<\/p>\n","protected":false},"author":37,"featured_media":28497,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[526,115,182,87,180,67,56,84,1303,1],"tags":[740,277,96],"acf":[],"yoast_head":"\nResearch Highlights: AutoDC: Automated Data-centric Processing - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2022\/02\/17\/research-highlights-autodc-automated-data-centric-processing\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Research Highlights: AutoDC: Automated Data-centric Processing - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"Most AutoML solutions are developed with a model-centric approach, however, according to a research paper, "AutoDC Automated Data-centric Processing," that was accepted into last year\u2019s highly selective NeurIPS conference on the development of\u00a0an automated data-centric tool (AutoDC), it was found to save an estimated 80% of the manual time needed for data set improvement\u00a0\u2013 typically a bespoke and costly process.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/insidebigdata.com\/2022\/02\/17\/research-highlights-autodc-automated-data-centric-processing\/\" \/>\n<meta property=\"og:site_name\" content=\"insideBIGDATA\" \/>\n<meta property=\"article:publisher\" content=\"http:\/\/www.facebook.com\/insidebigdata\" \/>\n<meta property=\"article:published_time\" content=\"2022-02-17T14:00:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-06-23T19:43:30+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2022\/02\/Hypergiant_logo.png\" \/>\n\t<meta property=\"og:image:width\" content=\"157\" \/>\n\t<meta property=\"og:image:height\" content=\"129\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Daniel Gutierrez\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@AMULETAnalytics\" \/>\n<meta name=\"twitter:site\" content=\"@insideBigData\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Daniel Gutierrez\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/insidebigdata.com\/2022\/02\/17\/research-highlights-autodc-automated-data-centric-processing\/\",\"url\":\"https:\/\/insidebigdata.com\/2022\/02\/17\/research-highlights-autodc-automated-data-centric-processing\/\",\"name\":\"Research Highlights: AutoDC: Automated Data-centric Processing - 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