<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Online Machine Learning on Jaehyeon Kim</title><link>https://jaehyeon.me/tags/online-machine-learning/</link><description>Recent content in Online Machine Learning on Jaehyeon Kim</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>Copyright © 2023-2026 Jaehyeon Kim. All Rights Reserved.</copyright><lastBuildDate>Tue, 21 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://jaehyeon.me/tags/online-machine-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>Building a Real-Time Industrial Digital Twin with Apache Flink and Online Machine Learning</title><link>https://jaehyeon.me/blog/2026-04-21-digital-twin-online-machine-learning/</link><pubDate>Tue, 21 Apr 2026 00:00:00 +0000</pubDate><guid>https://jaehyeon.me/blog/2026-04-21-digital-twin-online-machine-learning/</guid><description>Overview In heavy industrial manufacturing, such as steel hot strip rolling, deterministic physics formulas are the traditional standard for calculating the exact force required to deform a slab of steel. However, these pure physics models share a fatal flaw: they assume a pristine factory state. As physical rollers grind against red-hot steel over hours of production, they experience mechanical wear.
As the machinery degrades, the actual physical force required drifts away from the theoretical prediction.</description><enclosure url="https://jaehyeon.me/blog/2026-04-21-digital-twin-online-machine-learning/featured.png" length="130846" type="image/png"/></item></channel></rss>