<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Model Versioning on Formize.com Blog</title><link>https://blog.formize.com/tags/model-versioning/</link><description>Recent content in Model Versioning on Formize.com Blog</description><generator>Hugo</generator><language>en</language><atom:link href="https://blog.formize.com/tags/model-versioning/index.xml" rel="self" type="application/rss+xml"/><item><title>Accelerating AI Model Versioning and Change Management with Formize</title><link>https://blog.formize.com/accelerating-ai-model-versioning-and-change-management-with/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://blog.formize.com/accelerating-ai-model-versioning-and-change-management-with/</guid><description>&lt;h1 id="accelerating-ai-model-versioning-and-change-management-with-formize">Accelerating AI Model Versioning and Change Management with Formize&lt;/h1>
&lt;p>Artificial intelligence (AI) models are no longer experimental prototypes; they are production‑grade assets that drive revenue, influence customer experience, and, in many sectors, carry regulatory obligations. As models evolve—through data updates, hyper‑parameter tuning, architecture changes, or re‑training—organizations must answer three critical questions:&lt;/p>
&lt;ol>
&lt;li>&lt;strong>Which version of the model is currently in production?&lt;/strong>&lt;/li>
&lt;li>&lt;strong>What changes were introduced, and why?&lt;/strong>&lt;/li>
&lt;li>&lt;strong>Can we prove compliance with internal policies and external regulations?&lt;/strong>&lt;/li>
&lt;/ol>
&lt;p>Traditional approaches rely on ad‑hoc spreadsheets, manual change‑request tickets, or fragmented version‑control systems that do not capture the full governance context. The result is a brittle audit trail, delayed releases, and heightened risk of non‑compliance.&lt;/p></description></item></channel></rss>