<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>PAI on Chen Kai Blog</title><link>https://www.chenk.top/en/tags/pai/</link><description>Recent content in PAI on Chen Kai Blog</description><generator>Hugo</generator><language>en</language><lastBuildDate>Fri, 08 May 2026 09:00:00 +0000</lastBuildDate><atom:link href="https://www.chenk.top/en/tags/pai/index.xml" rel="self" type="application/rss+xml"/><item><title>Alibaba Cloud Full Stack (11): PAI — The ML Platform</title><link>https://www.chenk.top/en/aliyun-fullstack/11-pai-ml-platform/</link><pubDate>Fri, 08 May 2026 09:00:00 +0000</pubDate><guid>https://www.chenk.top/en/aliyun-fullstack/11-pai-ml-platform/</guid><description>&lt;p>Training a model on a single GPU is fun. Deploying it to handle 1,000 requests per second without failing is what separates experiments from products. PAI handles both.&lt;/p>
&lt;p>PAI (Platform for AI) is Alibaba Cloud&amp;rsquo;s managed ML platform. It&amp;rsquo;s not just one product; it&amp;rsquo;s five products in a trench coat, sharing a console. These include a notebook environment for exploration, a distributed training service for scale, a model serving platform for production, a visual pipeline designer for those who prefer dragging boxes, and a model gallery for one-click deployment of open-source models. After eighteen months of running real LLM workloads on it, I can say that the individual components range from excellent (EAS) to good enough (Designer). The whole platform is genuinely greater than the sum of its parts once you understand how they connect.&lt;/p></description></item></channel></rss>