{"id":1230,"date":"2025-01-16T07:02:31","date_gmt":"2025-01-16T07:02:31","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/01\/16\/learnings-from-a-machine-learning-engineer-part-1-the-data-08d8d0ecebcd\/"},"modified":"2025-01-16T07:02:31","modified_gmt":"2025-01-16T07:02:31","slug":"learnings-from-a-machine-learning-engineer-part-1-the-data-08d8d0ecebcd","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/01\/16\/learnings-from-a-machine-learning-engineer-part-1-the-data-08d8d0ecebcd\/","title":{"rendered":"Learnings from a Machine Learning Engineer\u200a\u2014\u200aPart 1: The Data"},"content":{"rendered":"<p>    Learnings from a Machine Learning Engineer\u200a\u2014\u200aPart 1: The Data<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n    <!-- no image --><br \/>\n \t<BR><br \/>\n<BR><\/BR><\/p>\n<div>\n<div class=\"medium-feed-item\">\n<p class=\"medium-feed-image\"><a href=\"https:\/\/towardsdatascience.com\/learnings-from-a-machine-learning-engineer-part-1-the-data-08d8d0ecebcd\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/2600\/0*qvxZ87TH47Cd-FqT\" width=\"4195\"><\/a><\/p>\n<p class=\"medium-feed-snippet\">Practical insights for a data-driven approach to model optimization<\/p>\n<p class=\"medium-feed-link\"><a href=\"https:\/\/towardsdatascience.com\/learnings-from-a-machine-learning-engineer-part-1-the-data-08d8d0ecebcd\">Continue reading on Towards Data Science \u00bb<\/a><\/p>\n<\/div>\n<\/div>\n<p> \t<BR><br \/>\n <BR><\/BR><br \/>\n    David Martin<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/medium.com\/m\/global-identity-2?redirectUrl=https%3A%2F%2Ftowardsdatascience.com%2Flearnings-from-a-machine-learning-engineer-part-1-the-data-08d8d0ecebcd\">Go to original source<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Learnings from a Machine Learning Engineer\u200a\u2014\u200aPart 1: The Data Practical insights for a data-driven approach to model optimization Continue reading on Towards Data Science \u00bb David Martin Go to original source<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,83,254,1322,70,909],"tags":[84,1323,341],"class_list":["post-1230","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-data-science","category-dataset","category-image-classification","category-machine-learning","category-machine-learning-engineer","tag-data","tag-learnings","tag-machine"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/1230"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=1230"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/1230\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=1230"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=1230"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=1230"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}