{"id":1255,"date":"2025-01-17T07:02:36","date_gmt":"2025-01-17T07:02:36","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/01\/17\/learnings-from-a-machine-learning-engineer-part-3-the-evaluation-e4a8dbb035e0\/"},"modified":"2025-01-17T07:02:36","modified_gmt":"2025-01-17T07:02:36","slug":"learnings-from-a-machine-learning-engineer-part-3-the-evaluation-e4a8dbb035e0","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/01\/17\/learnings-from-a-machine-learning-engineer-part-3-the-evaluation-e4a8dbb035e0\/","title":{"rendered":"Learnings from a Machine Learning Engineer\u200a\u2014\u200aPart 3: The Evaluation"},"content":{"rendered":"<p>    Learnings from a Machine Learning Engineer\u200a\u2014\u200aPart 3: The Evaluation<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-3-the-evaluation-e4a8dbb035e0\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/2600\/0*4u6UoDTGeMm9CbyC\" width=\"5964\"><\/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-3-the-evaluation-e4a8dbb035e0\">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-3-the-evaluation-e4a8dbb035e0\">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 3: The Evaluation 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":[62,166,1322,70,909,1343],"tags":[199,1323,341],"class_list":["post-1255","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-hands-on-tutorials","category-image-classification","category-machine-learning","category-machine-learning-engineer","category-process-improvement","tag-learning","tag-learnings","tag-machine"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/1255"}],"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=1255"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/1255\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=1255"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=1255"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=1255"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}