<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Feature-Store on Pi Stack</title><link>https://www.pistack.xyz/tags/feature-store/</link><description>Recent content in Feature-Store on Pi Stack</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sun, 19 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://www.pistack.xyz/tags/feature-store/index.xml" rel="self" type="application/rss+xml"/><item><title>Feast vs Featureform vs Hopsworks: Best Self-Hosted ML Feature Store 2026</title><link>https://www.pistack.xyz/posts/feast-vs-featureform-vs-hopsworks-self-hosted-ml-feature-store-2026/</link><pubDate>Sun, 19 Apr 2026 00:00:00 +0000</pubDate><guid>https://www.pistack.xyz/posts/feast-vs-featureform-vs-hopsworks-self-hosted-ml-feature-store-2026/</guid><description>&lt;p>A &lt;strong>feature store&lt;/strong> is a centralized platform that manages, stores, and serves machine learning features for both training and inference. It solves one of the most common pain points in production ML: the gap between how features are computed during experimentation versus how they are served in production. Without a feature store, data science teams often rebuild feature pipelines from scratch for every model, leading to training-serving skew, duplicated effort, and inconsistent results.&lt;/p></description></item></channel></rss>