<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Platform Engineering on StackSimplify | DevOps &amp; Cloud Education by Kalyan Reddy</title><link>https://stacksimplify.com/tags/platform-engineering/</link><description>Recent content in Platform Engineering on StackSimplify | DevOps &amp; Cloud Education by Kalyan Reddy</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><lastBuildDate>Tue, 05 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://stacksimplify.com/tags/platform-engineering/index.xml" rel="self" type="application/rss+xml"/><item><title>MLOps Maturity Model: From Notebooks to Platform in 5 Levels</title><link>https://stacksimplify.com/blog/mlops-maturity-model/</link><pubDate>Tue, 05 May 2026 00:00:00 +0000</pubDate><guid>https://stacksimplify.com/blog/mlops-maturity-model/</guid><description>Level 0: Jupyter notebook in production. Level 4: Fully automated ML lifecycle.
Most teams think they are somewhere in the middle. Most teams are wrong.
Here is the MLOps Maturity Model. Five levels, from chaos to platform.
The Five Levels Level Name What It Looks Like 0 Manual Notebooks copied to prod. No versioning. Single person dependency. 1 Managed Model registry, basic monitoring, manual retraining with a process. 2 Automated CI/CD pipelines, automated retraining triggers, quality gates.</description></item></channel></rss>