<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Feature Store on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/feature-store/</link><description>Recent content in Feature Store on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Wed, 28 Jan 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/feature-store/index.xml" rel="self" type="application/rss+xml"/><item><title>Project: Developing a Feature Store with MetaDataFlow</title><link>https://ai-blog.noorshomelab.dev/metadataflow-guide-2026/15-project-feature-store/</link><pubDate>Wed, 28 Jan 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/metadataflow-guide-2026/15-project-feature-store/</guid><description>&lt;h2 id="introduction"&gt;Introduction&lt;/h2&gt;
&lt;p&gt;Welcome to Chapter 15! So far, we&amp;rsquo;ve explored the foundational concepts of MetaDataFlow, a powerful (and for the purposes of this guide, hypothetical) open-source library from Meta AI designed to streamline dataset management for machine learning. We&amp;rsquo;ve seen how it can help you define, version, and orchestrate your data pipelines. Now, it&amp;rsquo;s time to put those skills to the test by tackling a crucial MLOps component: building a Feature Store.&lt;/p&gt;</description></item></channel></rss>