{"id":4892,"date":"2026-03-02T10:15:37","date_gmt":"2026-03-02T09:15:37","guid":{"rendered":"https:\/\/data4success.de\/?p=4892"},"modified":"2026-01-21T10:54:14","modified_gmt":"2026-01-21T09:54:14","slug":"machine-learning-rethought-experiments-in-fabric-for-dynamics-data","status":"publish","type":"post","link":"https:\/\/data4success.de\/en\/machine-learning-rethought-experiments-in-fabric-for-dynamics-data\/","title":{"rendered":"Machine learning rethought: experiments in Fabric for Dynamics data"},"content":{"rendered":"<div data-elementor-type=\"wp-post\" data-elementor-id=\"4892\" class=\"elementor elementor-4892\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-d4257f6 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d4257f6\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-2443f97\" data-id=\"2443f97\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c8bb6cf elementor-widget elementor-widget-text-editor\" data-id=\"c8bb6cf\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Machine learning (ML) is a central component of modern data analysis. In <strong>Microsoft Fabric<\/strong> there is a separate artifact for this: <strong>Experiments<\/strong>.<br \/>They serve as a systematic environment in which data scientists can compare, test, optimize and version ML models - including all training runs, errors, hyperparameters and performance metrics.<\/p><div><p>Especially when companies use data from Microsoft Dynamics NAV, Navision or Business Central, a clean, reproducible experimentation environment is crucial. Why is that?<br \/>Because BC\/NAV data often contains complex patterns (e.g. in customer, article, financial or booking data) that require precise models for forecasting, churn risks or liquidity planning.<\/p><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-33ce3a8 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"33ce3a8\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-a4a207f\" data-id=\"a4a207f\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-0bc6f36 elementor-widget elementor-widget-text-editor\" data-id=\"0bc6f36\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><style>\na {\n    text-decoration: none;\n    color: #464feb;\n}\ntr th, tr td {\n    border: 1px solid #e6e6e6;\n}\ntr th {\n    background-color: #f5f5f5;\n}\n<\/style><\/p><div><h3>What is an experiment in Microsoft Fabric?<\/h3><p>A <em>Experiment<\/em> is a structured collection of:<\/p><ul><li>different ML model versions<\/li><li>Results of individual training runs<\/li><li>KPIs such as Accuracy, R\u00b2 or Loss<\/li><li>Metadata such as <code>trial_time<\/code> or feature configurations<\/li><\/ul><p>It therefore serves as a \u201elaboratory\u201c for the entire training and optimization process.<\/p><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-2916957 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2916957\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4483068\" data-id=\"4483068\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7faab5b elementor-widget elementor-widget-text-editor\" data-id=\"7faab5b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3>The experiment process: train, test, repeat<\/h3><div><p>Fabric describes the experiment workflow as a repeated cycle:<\/p><ol><li><strong>Training<\/strong> - Model is adapted to training data<\/li><li><strong>Evaluation<\/strong> - Model performance is measured<\/li><li><strong>Testing<\/strong> - Validation on unknown data<\/li><li><strong>Iteration<\/strong> - Experiments are repeated until a satisfactory result is achieved<\/li><\/ol><p>An experiment saves each of these iterations - perfect for reproducible data science work.<\/p><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-4d87f58 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"4d87f58\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3321464\" data-id=\"3321464\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-1278ca5 elementor-widget elementor-widget-text-editor\" data-id=\"1278ca5\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3>What is stored in an experiment?<\/h3><div><p>The experiments contain:<\/p><ul><li>the model itself<\/li><li>all model versions<\/li><li>the results of each run<\/li><li>important metrics such as <strong>R-Squared (R\u00b2)<\/strong><\/li><li>Runtime information such as <strong>trial_time<\/strong><\/li><\/ul><p>This allows data scientists to understand exactly which models work well and why.<\/p><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6dcad38 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6dcad38\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-8dc00da\" data-id=\"8dc00da\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c13c239 elementor-widget elementor-widget-text-editor\" data-id=\"c13c239\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3>What happens after the experiment?<\/h3><div><p>When the optimal model has been determined:<\/p><ul><li>A <strong>Notebook<\/strong> retrieves the model stored in Fabric<\/li><li>It generates <em>regularly<\/em> Predictions<\/li><li>The predicted values are saved in a table in the Lakehouse<\/li><li>Power BI uses this data directly for dashboards<\/li><\/ul><p>This means that ML <strong>Fully operationalized<\/strong> - without external tools.<\/p><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-543973b elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"543973b\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-a108194\" data-id=\"a108194\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-fa5fdb8 elementor-widget elementor-widget-text-editor\" data-id=\"fa5fdb8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>These systems provide extensive historical data ideal for forecasts, risk analyses or customer models. Seamless integration with Lakehouse, Notebooks and Pipelines - as also described in your internal project documents - means that experiments can be optimally operationalized and holistically integrated into your fabric architecture.<\/p><div><p>This makes Fabric a platform that not only trains ML models, but also makes them reliably usable in productive business processes. Companies that rely on BC\/NAV data can thus accelerate data-based decisions, automate processes and integrate AI-supported insights directly into Power BI and operational workflows. Experiments are therefore a central key to fully exploiting the potential of machine learning in the Dynamics environment.<\/p><\/div><p><strong>Do you have any questions or would you like to find out more about our methods?<\/strong> <a href=\"https:\/\/data4success.de\/en\/contact\/\">Get in touch with us<\/a> - We show you how you can use your data for sustainable success.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"<p>Machine Learning (ML) ist ein zentraler Bestandteil moderner Datenanalyse. In Microsoft Fabric gibt es daf\u00fcr ein eigenes Artefakt: Experiments. Sie dienen als systematische Umgebung, in der Data Scientists ML\u2011Modelle vergleichen, testen, optimieren und versionieren k\u00f6nnen \u2013 inklusive aller Trainingsl\u00e4ufe, Fehler, Hyperparameter und Leistungsmetriken. Gerade wenn Unternehmen Daten aus Microsoft Dynamics NAV, Navision oder Business Central [&hellip;]<\/p>\n","protected":false},"author":4,"featured_media":4897,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-4892","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/posts\/4892","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/comments?post=4892"}],"version-history":[{"count":9,"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/posts\/4892\/revisions"}],"predecessor-version":[{"id":4902,"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/posts\/4892\/revisions\/4902"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/media\/4897"}],"wp:attachment":[{"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/media?parent=4892"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/categories?post=4892"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/tags?post=4892"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}