<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Kubeflow on StackSimplify | DevOps &amp; Cloud Education by Kalyan Reddy</title><link>https://stacksimplify.com/tags/kubeflow/</link><description>Recent content in Kubeflow on StackSimplify | DevOps &amp; Cloud Education by Kalyan Reddy</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><lastBuildDate>Tue, 14 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://stacksimplify.com/tags/kubeflow/index.xml" rel="self" type="application/rss+xml"/><item><title>ML Pipeline Orchestration with Kubeflow on Kubernetes</title><link>https://stacksimplify.com/blog/kubeflow-pipelines-orchestration/</link><pubDate>Tue, 14 Apr 2026 00:00:00 +0000</pubDate><guid>https://stacksimplify.com/blog/kubeflow-pipelines-orchestration/</guid><description>Your ML team has 47 Jupyter notebooks. 12 of them &amp;ldquo;should run in order.&amp;rdquo; Nobody remembers which 12.
One fetches data. Another cleans it. A third trains. A fourth evaluates. A fifth deploys. Different repos. Hardcoded paths. Two only work on Sarah&amp;rsquo;s laptop.
This is not a pipeline. This is a disaster waiting for a deadline.
Why ML Pipelines Are Different Data pipelines move data from A to B. ETL. Airflow handles this well.</description></item></channel></rss>