<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Microsoft Fabric on Data Simplicity</title><link>https://nobledynamic.github.io/tags/microsoft-fabric/</link><description>Recent content in Microsoft Fabric on Data Simplicity</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>© 2026 Noble Dynamic Limited</copyright><lastBuildDate>Mon, 29 Apr 2024 11:40:43 +0000</lastBuildDate><atom:link href="https://nobledynamic.github.io/tags/microsoft-fabric/index.xml" rel="self" type="application/rss+xml"/><item><title>Fabric Madness: Models</title><link>https://nobledynamic.github.io/posts/fabric-madness-5/</link><pubDate>Mon, 29 Apr 2024 11:40:43 +0000</pubDate><guid>https://nobledynamic.github.io/posts/fabric-madness-5/</guid><description>Our fifth and final post in the Fabric series, where we dive into model registries, which are essential for production scenarios.</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://nobledynamic.github.io/posts/fabric-madness-5/feature.webp"/></item><item><title>Fabric Madness: Experiments</title><link>https://nobledynamic.github.io/posts/fabric-madness-4/</link><pubDate>Mon, 22 Apr 2024 11:38:43 +0000</pubDate><guid>https://nobledynamic.github.io/posts/fabric-madness-4/</guid><description>Part 4 of our series on Fabric, this time looking at experiments, a tool that allows for iterative development of Machine Learning Systems in an experimental way.</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://nobledynamic.github.io/posts/fabric-madness-4/feature.webp"/></item><item><title>Fabric Madness: Feature Engineering with Dataflow Gen2</title><link>https://nobledynamic.github.io/posts/fabric-madness-3/</link><pubDate>Mon, 15 Apr 2024 11:37:43 +0000</pubDate><guid>https://nobledynamic.github.io/posts/fabric-madness-3/</guid><description>In part 3 of this series we look again at feature engineering, this time with Dataflow Gen2; a low code data transformation and integration engine.</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://nobledynamic.github.io/posts/fabric-madness-3/feature.webp"/></item><item><title>Fabric Madness: Feature Engineering with pyspark</title><link>https://nobledynamic.github.io/posts/fabric-madness-2/</link><pubDate>Mon, 08 Apr 2024 09:37:43 +0000</pubDate><guid>https://nobledynamic.github.io/posts/fabric-madness-2/</guid><description>In part 2 of this series we dive deeper into the process of feature engineering, a crucial part of the development lifecycle for any Machine Learning (ML) systems.</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://nobledynamic.github.io/posts/fabric-madness-2/feature.webp"/></item><item><title>Fabric Madness: predicting basketball games with Microsoft Fabric</title><link>https://nobledynamic.github.io/posts/fabric-madness-1/</link><pubDate>Mon, 01 Apr 2024 19:00:43 +0000</pubDate><guid>https://nobledynamic.github.io/posts/fabric-madness-1/</guid><description>In this series of posts titled Fabric Madness, we&amp;rsquo;re going to be diving deep into some of the most interesting features of Microsoft Fabric, for an end-to-end demonstration of how to train and use a machine learning model.</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://nobledynamic.github.io/posts/fabric-madness-1/feature.webp"/></item></channel></rss>