<?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>Machine-Learning on Miguel Lameiro | Cybersecurity Blog &amp; Security Writeups</title><link>https://blog.lameiro0x.com/tags/machine-learning/</link><description>Recent content in Machine-Learning on Miguel Lameiro | Cybersecurity Blog &amp; Security Writeups</description><generator>Hugo -- 0.161.1</generator><language>en-us</language><lastBuildDate>Tue, 05 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://blog.lameiro0x.com/tags/machine-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>LLMOps Pipeline: From Dataset Preparation to Safe Prediction</title><link>https://blog.lameiro0x.com/notes/ai-security/llmops-pipeline-from-data-to-safe-prediction/</link><pubDate>Tue, 05 May 2026 00:00:00 +0000</pubDate><guid>https://blog.lameiro0x.com/notes/ai-security/llmops-pipeline-from-data-to-safe-prediction/</guid><description>&lt;h2 id="introduction"&gt;Introduction&lt;/h2&gt;
&lt;p&gt;LLMOps is the operational layer around LLM applications. It is not only model deployment. A useful LLM workflow also needs data preparation, artifact versioning, orchestration, prompt consistency, endpoint management, safety checks, and monitoring.&lt;/p&gt;
&lt;p&gt;The main lesson from this course is that an LLM application becomes production-ready only when the surrounding system is controlled. The model is one component. The data pipeline, prompt format, evaluation split, deployment strategy, and response metadata are just as important.&lt;/p&gt;</description></item></channel></rss>