<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Production on English AI Terms Dictionary</title><link>https://terms-en.ai-term-hub.com/en/tags/production/</link><description>Recent content in Production on English AI Terms Dictionary</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 18 Jul 2026 11:44:44 +0000</lastBuildDate><atom:link href="https://terms-en.ai-term-hub.com/en/tags/production/index.xml" rel="self" type="application/rss+xml"/><item><title>Inference</title><link>https://terms-en.ai-term-hub.com/en/terms/inference/</link><pubDate>Sat, 18 Jul 2026 07:39:00 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/inference/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>Inference refers to the deployment stage where a finalized model is used to make decisions or predictions on unseen data. Unlike training, which updates weights, inference consumes computational resources to execute forward passes through the network. Optimizing inference is crucial for latency, cost, and scalability in production environments, often involving techniques like quantization, pruning, or batching to ensure efficient real-time performance.&lt;/p>
&lt;h3 id="summary">Summary&lt;/h3>
&lt;p>The phase where a trained model processes new data to generate predictions or outputs.&lt;/p></description></item></channel></rss>