<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>celery on Ritesh Sonawane</title><link>https://riteshsonawane.com/tags/celery/</link><description>Recent content in celery on Ritesh Sonawane</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Tue, 09 Dec 2025 00:00:00 +0000</lastBuildDate><atom:link href="https://riteshsonawane.com/tags/celery/index.xml" rel="self" type="application/rss+xml"/><item><title>Celery to Argo Workflows</title><link>https://riteshsonawane.com/blog/celery-to-argoworkflows/</link><pubDate>Tue, 09 Dec 2025 00:00:00 +0000</pubDate><guid>https://riteshsonawane.com/blog/celery-to-argoworkflows/</guid><description>&lt;p&gt;&lt;strong&gt;This blog is based on my work at CloudRaft!&lt;/strong&gt;&lt;/p&gt;
&lt;p&gt;AI jobs often run for long periods on expensive hardware like GPUs. When a job fails halfway, you don&amp;rsquo;t just lose progress—you waste valuable time and costly resources. Workflow orchestration solves this by providing fault tolerance, letting you break complex tasks into manageable steps, set dependencies, and recover from failures. This is especially critical in machine learning, where robust, efficient execution is paramount.&lt;/p&gt;</description></item></channel></rss>