<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>AI on nclsbayona's blog!</title><link>https://nclsbayona.github.io/categories/ai/</link><description>Recent content in AI on nclsbayona's blog!</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><lastBuildDate>Wed, 10 Jun 2026 20:00:00 +0000</lastBuildDate><atom:link href="https://nclsbayona.github.io/categories/ai/index.xml" rel="self" type="application/rss+xml"/><item><title>Cloud-Native Lessons for AI: Applying Kubernetes Practices to Intelligent Systems</title><link>https://nclsbayona.github.io/p/cloud-native-lessons-ai-kubernetes/</link><pubDate>Sat, 09 May 2026 13:14:26 +0000</pubDate><guid>https://nclsbayona.github.io/p/cloud-native-lessons-ai-kubernetes/</guid><description>&lt;h1 id="cloud-native-lessons-for-ai-applying-kubernetes-practices-to-intelligent-systems">Cloud-Native Lessons for AI: Applying Kubernetes Practices to Intelligent Systems
&lt;/h1>&lt;p>Cloud-native technologies like Kubernetes have transformed application deployment and management. These principles offer valuable lessons for AI implementations, enabling more robust, scalable, and efficient intelligent systems. This post explores key lessons from cloud-native environments that apply to AI, complete with use cases, academic research, and best practices for DevOps teams.&lt;/p>
&lt;h2 id="lesson-1-declarative-configuration-for-ai-pipelines">Lesson 1: Declarative Configuration for AI Pipelines
&lt;/h2>&lt;p>Kubernetes&amp;rsquo; declarative approach defines desired states rather than imperative steps. In AI, this translates to defining ML pipelines as code, ensuring reproducibility and version control.&lt;/p></description></item></channel></rss>