<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Data Engineering on RhinoInsight</title><link>https://obogazkaya.me/tags/data-engineering/</link><description>Recent content in Data Engineering on RhinoInsight</description><image><title>RhinoInsight</title><url>https://obogazkaya.me/images/og-image.png</url><link>https://obogazkaya.me/images/og-image.png</link></image><generator>Hugo -- 0.152.2</generator><language>de-de</language><lastBuildDate>Sat, 14 Feb 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://obogazkaya.me/tags/data-engineering/index.xml" rel="self" type="application/rss+xml"/><item><title>Das Factory Model: Wie man ML-Systeme systematisch verbessert</title><link>https://obogazkaya.me/blog/factory-model-machine-learning/</link><pubDate>Sat, 14 Feb 2026 00:00:00 +0000</pubDate><guid>https://obogazkaya.me/blog/factory-model-machine-learning/</guid><description>Das Factory Model beschreibt ML-Entwicklung als zyklischen Prozess: Daten sammeln, labeln, trainieren, evaluieren, verbessern, wiederholen. Ein Framework für nachhaltige ML-Systeme.</description></item></channel></rss>