<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>MLOps on Blog Formize.com</title><link>https://blog.formize.com/cs/categories/mlops/</link><description>Recent content in MLOps on Blog Formize.com</description><generator>Hugo</generator><language>cs</language><atom:link href="https://blog.formize.com/cs/categories/mlops/index.xml" rel="self" type="application/rss+xml"/><item><title>Kontinuální správa dat v MLOps pipelinech s Formize</title><link>https://blog.formize.com/cs/continuous-data-governance-in-mlops/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://blog.formize.com/cs/continuous-data-governance-in-mlops/</guid><description>&lt;h1 id="kontinuální-správa-dat-v-mlops-pipelinech-s-formize">Kontinuální správa dat v MLOps pipelinech s Formize&lt;/h1>
&lt;p>Enterprises that ship machine‑learning models at scale face a paradox: the faster they iterate, the harder it becomes to guarantee that data used for training, validation, and inference complies with internal policies and external regulations. Traditional data‑governance approaches—manual audits, periodic reports, and static lineage maps—cannot keep up with the velocity of modern MLOps workflows.&lt;/p>
&lt;p>Formize, a low‑code data‑lineage and compliance engine, was built for exactly this challenge. By embedding Formize into the CI/CD pipeline, organizations can &lt;strong>capture lineage in real time&lt;/strong>, &lt;strong>enforce policy as code&lt;/strong>, and &lt;strong>expose quality dashboards&lt;/strong> that developers and auditors can query instantly.&lt;/p></description></item></channel></rss>