<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Airflow on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/airflow/</link><description>Recent content in Airflow 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/airflow/index.xml" rel="self" type="application/rss+xml"/><item><title>Project: Deploying a Production-Ready Data Workflow</title><link>https://ai-blog.noorshomelab.dev/metadataflow-guide-2026/16-project-production-workflow/</link><pubDate>Wed, 28 Jan 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/metadataflow-guide-2026/16-project-production-workflow/</guid><description>&lt;h2 id="introduction-from-local-scripts-to-production-pipelines"&gt;Introduction: From Local Scripts to Production Pipelines&lt;/h2&gt;
&lt;p&gt;Welcome to Chapter 16! So far, you&amp;rsquo;ve mastered the core features of &lt;code&gt;MetaDataHub&lt;/code&gt;, Meta AI&amp;rsquo;s powerful open-source library for managing datasets. You&amp;rsquo;ve learned how to version, track lineage, and ensure data quality in isolated examples. But what happens when your data needs to move beyond your local machine and into a reliable, scalable, and automated production environment? That&amp;rsquo;s exactly what we&amp;rsquo;ll tackle in this chapter!&lt;/p&gt;</description></item></channel></rss>