{"id":1787,"date":"2017-01-13T14:16:33","date_gmt":"2017-01-13T22:16:33","guid":{"rendered":"http:\/\/engineering.siftscience.com\/?p=1787"},"modified":"2023-01-06T13:18:27","modified_gmt":"2023-01-06T21:18:27","slug":"conf-startup-ml","status":"publish","type":"post","link":"https:\/\/engineering.sift.com\/conf-startup-ml\/","title":{"rendered":"conf.startup.ml"},"content":{"rendered":"<p>We&#8217;re very proud to sponsor Startup ML&#8217;s conference on <a href=\"https:\/\/conf.startup.ml\">Putting Deep Learning into Production<\/a>, being held\u00a0Jan 21, 2017.<\/p>\n<p>While we won&#8217;t be presenting <a href=\"https:\/\/conf.startup.ml\/geekdomsf\/\">this time<\/a>, we&#8217;re excited to see the presentations dealing with the large-scale and complexity of effectively using Deep Learning models in production. In particular, <a href=\"https:\/\/github.com\/ilblackdragon\">Illia Polosukhin<\/a>&#8216;s talk on Finding Adversarial Examples &#8212; especially insofar as limiting exploitation of models &#8212; and\u00a0<a href=\"https:\/\/www.stat.berkeley.edu\/~mmahoney\/\">Michael Mahoney<\/a>&#8216;s talk, Computationally-intensive machine learning at the tera-scale, are relevant to the problems we work on.<\/p>\n<p>If you&#8217;d like to chat with one of us at the conference, please don&#8217;t hesitate to come up and say hello! We&#8217;ll be wearing our Sift Science T-shirts and we&#8217;d love to talk to you.<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"We&#8217;re very proud to sponsor Startup ML&#8217;s conference on Putting Deep Learning into Production, being held\u00a0Jan 21, 2017. While we won&#8217;t be presenting this time, we&#8217;re excited to see the presentations dealing with the large-scale and complexity of effectively using Deep Learning models in production. In particular, Illia Polosukhin&#8216;s talk on Finding Adversarial Examples &#8212; [&hellip;]","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[43],"tags":[],"ppma_author":[142],"class_list":["post-1787","post","type-post","status-publish","format-standard","hentry","category-talks"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>conf.startup.ml - Sift Engineering Blog<\/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:\/\/engineering.sift.com\/conf-startup-ml\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"conf.startup.ml - Sift Engineering Blog\" \/>\n<meta property=\"og:description\" content=\"We&#8217;re very proud to sponsor Startup ML&#8217;s conference on Putting Deep Learning into Production, being held\u00a0Jan 21, 2017. While we won&#8217;t be presenting this time, we&#8217;re excited to see the presentations dealing with the large-scale and complexity of effectively using Deep Learning models in production. 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