<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Ml on The Augmented Scholar</title><link>https://augmentedscholars.com/tags/ml/</link><description>Recent content in Ml on The Augmented Scholar</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 18 Jul 2026 16:33:26 +0400</lastBuildDate><atom:link href="https://augmentedscholars.com/tags/ml/index.xml" rel="self" type="application/rss+xml"/><item><title>DVC — Git for Data and Models</title><link>https://augmentedscholars.com/tools/dvc/</link><pubDate>Fri, 17 Jul 2026 00:00:00 +0000</pubDate><guid>https://augmentedscholars.com/tools/dvc/</guid><description>&lt;h2 id="overview"&gt;Overview&lt;/h2&gt;
&lt;p&gt;DVC extends Git discipline to data: &amp;lsquo;dvc add data/raw&amp;rsquo; stores a small pointer file in Git while the actual data goes to remote storage you control (S3, Drive, SSH, a NAS). Checkout any commit and &amp;lsquo;dvc pull&amp;rsquo; restores exactly the data that produced those results.&lt;/p&gt;</description></item><item><title>Weights &amp; Biases — Experiment Tracking for ML</title><link>https://augmentedscholars.com/tools/wandb/</link><pubDate>Fri, 17 Jul 2026 00:00:00 +0000</pubDate><guid>https://augmentedscholars.com/tools/wandb/</guid><description>&lt;h2 id="overview"&gt;Overview&lt;/h2&gt;
&lt;p&gt;W&amp;amp;B answers &amp;lsquo;which run produced this model, with which hyperparameters?&amp;rsquo; permanently: a few lines of code log every metric, config, and artifact to a dashboard where runs are compared, filtered, and shared. Free for academics, integrated with every major ML framework.&lt;/p&gt;</description></item><item><title>Hugging Face — The GitHub of Machine Learning</title><link>https://augmentedscholars.com/tools/hugging-face/</link><pubDate>Fri, 17 Jul 2026 00:00:00 +0000</pubDate><guid>https://augmentedscholars.com/tools/hugging-face/</guid><description>&lt;h2 id="overview"&gt;Overview&lt;/h2&gt;
&lt;p&gt;Hugging Face is research infrastructure: the transformers/datasets libraries load a million models and 200k datasets in a line of code; the Hub versions your own artifacts like Git; Spaces host interactive demos of your method that reviewers can try in a browser.&lt;/p&gt;</description></item></channel></rss>