{"id":14197,"date":"2026-06-22T04:13:16","date_gmt":"2026-06-22T08:13:16","guid":{"rendered":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/?p=14197"},"modified":"2026-06-22T04:15:12","modified_gmt":"2026-06-22T08:15:12","slug":"artifact-driven-development-making-it-possible-to-query-large-analytics-and-ai-projects","status":"publish","type":"post","link":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/artifact-driven-development-making-it-possible-to-query-large-analytics-and-ai-projects\/14197\/","title":{"rendered":"Artifact-Driven Development: Making It Possible to Query Large Analytics and AI Projects"},"content":{"rendered":"A practical introduction to making complex project structure explicit for humans and AI, with examples from predictive analytics and enterprise ML. Large analytics and AI projects contain more than source code. Predictive analytics and enterprise ML projects make this especially visible: they contain intermediate datasets, derived tables, feature definitions, model inputs, evaluation results, decisions, workflow state, dependencies, business rules, and operational constraints. Much of this structure is often left implicit: buried in notebooks, scripts, SQL, conventions, chat history, or in one person&#8217;s head. Artifact-driven development is a simple response: treat important intermediate products as explicit artifacts. In this <a href=\"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/artifact-driven-development-making-it-possible-to-query-large-analytics-and-ai-projects\/14197\/\" class=\"more-link\">(more&hellip;)<\/a>","protected":false},"excerpt":{"rendered":"<p>A practical introduction to making complex project structure explicit for humans and AI, with examples from predictive analytics and enterprise ML. Large analytics and AI projects contain more than source code. Predictive analytics and enterprise ML projects make this especially visible: they contain intermediate datasets, derived tables, feature definitions, model inputs, evaluation results, decisions, workflow [&hellip;]<\/p>\n","protected":false},"author":13468,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[3],"tags":[],"class_list":["post-14197","post","type-post","status-publish","format-standard","hentry","category-leading-stories"],"_links":{"self":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/14197","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/users\/13468"}],"replies":[{"embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/comments?post=14197"}],"version-history":[{"count":4,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/14197\/revisions"}],"predecessor-version":[{"id":14201,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/14197\/revisions\/14201"}],"wp:attachment":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/media?parent=14197"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/categories?post=14197"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/tags?post=14197"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}