<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Vectorization on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/vectorization/</link><description>Recent content in Vectorization on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Fri, 20 Mar 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/vectorization/index.xml" rel="self" type="application/rss+xml"/><item><title>Building Robust Pipelines: From Ingestion to Vectorization</title><link>https://ai-blog.noorshomelab.dev/multimodal-ai-guide-2026/building-robust-pipelines-ingestion-vectorization/</link><pubDate>Fri, 20 Mar 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/multimodal-ai-guide-2026/building-robust-pipelines-ingestion-vectorization/</guid><description>&lt;h2 id="introduction-to-multimodal-data-pipelines"&gt;Introduction to Multimodal Data Pipelines&lt;/h2&gt;
&lt;p&gt;Welcome back, future multimodal AI architects! In previous chapters, we laid the groundwork for understanding what multimodal AI is and why it&amp;rsquo;s so powerful. We&amp;rsquo;ve talked about the magic of combining different types of data – text, images, audio, and video – to build more intelligent and nuanced systems. But how does this raw, diverse data actually get transformed into something our sophisticated AI models can understand and process?&lt;/p&gt;</description></item></channel></rss>