<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Scheduling on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/scheduling/</link><description>Recent content in Scheduling on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Wed, 20 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/scheduling/index.xml" rel="self" type="application/rss+xml"/><item><title>Building Robust Workflows: Queues, Scheduling, and Long-Running Processes</title><link>https://ai-blog.noorshomelab.dev/triggerdev-v4-guide-2026/robust-workflows-queues-scheduling-long-running/</link><pubDate>Wed, 20 May 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/triggerdev-v4-guide-2026/robust-workflows-queues-scheduling-long-running/</guid><description>&lt;p&gt;In the world of modern applications, especially those involving AI agents or complex data processing, tasks often need to run reliably in the background, at specific times, or endure for extended periods without interruption. Imagine sending out millions of personalized emails, generating daily reports, or orchestrating a multi-step AI inference process. How do you ensure these operations complete successfully, even if your server crashes or an external API temporarily fails?&lt;/p&gt;</description></item><item><title>Orchestration &amp;amp; Scheduling Data Workflows</title><link>https://ai-blog.noorshomelab.dev/metadataflow-guide-2026/09-orchestration-scheduling/</link><pubDate>Wed, 28 Jan 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/metadataflow-guide-2026/09-orchestration-scheduling/</guid><description>&lt;h2 id="introduction-to-orchestration--scheduling-data-workflows"&gt;Introduction to Orchestration &amp;amp; Scheduling Data Workflows&lt;/h2&gt;
&lt;p&gt;Welcome back, future data wizard! In our journey so far, you&amp;rsquo;ve learned how to leverage Meta AI&amp;rsquo;s powerful open-source library to manage your machine learning datasets, from ingestion to transformation and validation. But what happens when your data grows, your models need frequent updates, and your processes become too complex to run manually? That&amp;rsquo;s where &lt;strong&gt;orchestration&lt;/strong&gt; and &lt;strong&gt;scheduling&lt;/strong&gt; come into play!&lt;/p&gt;
&lt;p&gt;This chapter will equip you with the knowledge and practical skills to automate and manage your data pipelines using industry-standard tools, seamlessly integrating them with the Meta AI dataset management library. We&amp;rsquo;ll explore why consistent data workflows are critical for robust machine learning systems and how to build them step-by-step. By the end, you&amp;rsquo;ll be able to design and implement automated data workflows, ensuring your ML models always have access to fresh, high-quality data.&lt;/p&gt;</description></item></channel></rss>