{"id":1256,"date":"2025-01-17T07:02:39","date_gmt":"2025-01-17T07:02:39","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/01\/17\/learnings-from-a-machine-learning-engineer-part-4-the-model-7f530bc91383\/"},"modified":"2025-01-17T07:02:39","modified_gmt":"2025-01-17T07:02:39","slug":"learnings-from-a-machine-learning-engineer-part-4-the-model-7f530bc91383","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/01\/17\/learnings-from-a-machine-learning-engineer-part-4-the-model-7f530bc91383\/","title":{"rendered":"Learnings from a Machine Learning Engineer\u200a\u2014\u200aPart 4: The Model"},"content":{"rendered":"<p>    Learnings from a Machine Learning Engineer\u200a\u2014\u200aPart 4: The Model<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-4-the-model-7f530bc91383\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/2600\/0*6zXowHhXAlQoJ2xz\" width=\"5184\"><\/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-4-the-model-7f530bc91383\">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-4-the-model-7f530bc91383\">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 4: The Model 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":[1345,1346,62,1344,70,909],"tags":[1323,341,103],"class_list":["post-1256","post","type-post","status-publish","format-standard","hentry","category-ai-model-deployment","category-ai-model-training","category-aimldsaimlds","category-image-cla","category-machine-learning","category-machine-learning-engineer","tag-learnings","tag-machine","tag-model"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/1256"}],"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=1256"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/1256\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=1256"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=1256"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=1256"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}