<?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>Data-Quality on Andrea Bozzo | Blog</title><link>https://andreabozzo.pages.dev/en/tags/data-quality/</link><description>Recent content in Data-Quality on Andrea Bozzo | Blog</description><generator>Hugo -- 0.147.0</generator><language>en-US</language><lastBuildDate>Mon, 23 Mar 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://andreabozzo.pages.dev/en/tags/data-quality/index.xml" rel="self" type="application/rss+xml"/><item><title>Guardrails for Tabular ML: A Data Engineer's Take on Data Leakage, Poisoning, and Brittle Pipelines</title><link>https://andreabozzo.pages.dev/en/posts/tabularmlpipes-blog/</link><pubDate>Mon, 23 Mar 2026 00:00:00 +0000</pubDate><guid>https://andreabozzo.pages.dev/en/posts/tabularmlpipes-blog/</guid><description>Most ML pipeline failures are not exotic model bugs — they are data issues that nobody encoded as checks. This article walks through building guardrails using pandas, Apache DataFusion, data contracts, and the Arrow C Data Interface.</description></item></channel></rss>