<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Configurations on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/configurations/</link><description>Recent content in Configurations on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Thu, 01 Jan 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/configurations/index.xml" rel="self" type="application/rss+xml"/><item><title>Chapter 3: Logging Metrics, Parameters, and Configs</title><link>https://ai-blog.noorshomelab.dev/trackio-2026-guide/03-logging-metrics-and-parameters/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/trackio-2026-guide/03-logging-metrics-and-parameters/</guid><description>&lt;h2 id="introduction-to-logging-your-ml-story"&gt;Introduction to Logging Your ML Story&lt;/h2&gt;
&lt;p&gt;Welcome to Chapter 3! In the previous chapter, we got Trackio up and running and initialized our first experiment. Now, it&amp;rsquo;s time to make that experiment meaningful by recording what truly matters: your model&amp;rsquo;s performance, the settings you used, and the decisions you made along the way.&lt;/p&gt;
&lt;p&gt;This chapter is all about teaching you the art of logging. You&amp;rsquo;ll learn how to capture crucial information like metrics (how well your model is doing), parameters (the knobs and dials you tweaked), and configurations (the overall setup of your experiment). Think of it as writing a detailed lab report for every single machine learning run, but Trackio does most of the heavy lifting!&lt;/p&gt;</description></item></channel></rss>