<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Machine-Learning on Corey Daley</title><link>https://coreydaley.dev/tags/machine-learning/</link><description>Recent content in Machine-Learning on Corey Daley</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Thu, 16 Apr 2026 18:35:00 -0400</lastBuildDate><atom:link href="https://coreydaley.dev/tags/machine-learning/rss.xml" rel="self" type="application/rss+xml"/><item><title>A 12-Month AI/ML Roadmap for Engineers Who Feel Behind</title><link>https://coreydaley.dev/posts/2026/04/12-month-ai-ml-learning-roadmap/</link><pubDate>Thu, 16 Apr 2026 18:35:00 -0400</pubDate><guid>https://coreydaley.dev/posts/2026/04/12-month-ai-ml-learning-roadmap/</guid><description>&lt;p&gt;Every senior engineer I know has a version of the same conversation with themselves: &amp;ldquo;I should really learn more about ML.&amp;rdquo; It comes up during a planning meeting when someone mentions embeddings. It comes up when a job description at an interesting company lists MLOps as a requirement. Then the sprint board calls it back.&lt;/p&gt;
&lt;p&gt;I&amp;rsquo;ve published a 12-month AI/ML learning roadmap designed specifically for experienced software engineers — not a beginner tutorial, but a structured path from ML foundations through LLMs and generative AI, ML engineering at scale, and a capstone that turns a year of steady work into visible career leverage. The core idea: AI/ML becomes career-changing when it compounds through one sustained body of work, not when it&amp;rsquo;s consumed as scattered content.&lt;/p&gt;
&lt;p&gt;If you&amp;rsquo;ve been sitting on the feeling that you should be doing something about this — most of the resources are free, the rest are investments worth making, and the plan is already written.&lt;/p&gt;
&lt;p&gt;Read more at &lt;a
 href="https://coreydaley.dev/posts/2026/04/12-month-ai-ml-learning-roadmap/" target="_blank" rel="noopener noreferrer"&gt;https://coreydaley.dev/posts/2026/04/12-month-ai-ml-learning-roadmap/&lt;/a&gt;
&lt;/p&gt;</description></item><item><title>Seeing How the Sausage Gets Made: Demystifying AI and LLMs</title><link>https://coreydaley.dev/posts/2026/02/how-llms-work-sausage-making/</link><pubDate>Fri, 20 Feb 2026 09:00:00 -0500</pubDate><guid>https://coreydaley.dev/posts/2026/02/how-llms-work-sausage-making/</guid><description>&lt;p&gt;There&amp;rsquo;s a moment every developer eventually hits when they stop treating AI as a magic oracle and start asking: okay, but how does it actually work? It&amp;rsquo;s the technology equivalent of learning Santa isn&amp;rsquo;t real. A little wonder leaves the room, but something better moves in: understanding.&lt;/p&gt;
&lt;p&gt;And understanding turns out to be a surprisingly effective antidote to the kind of fear that has people picturing Skynet every time a chatbot gives a confident answer. So let&amp;rsquo;s look inside the machine — and maybe, along the way, inside ourselves.&lt;/p&gt;
&lt;p&gt;What do you think when you finally see how the sausage gets made?&lt;/p&gt;
&lt;p&gt;Read more at &lt;a
 href="https://coreydaley.dev/posts/2026/02/how-llms-work-sausage-making/" target="_blank" rel="noopener noreferrer"&gt;https://coreydaley.dev/posts/2026/02/how-llms-work-sausage-making/&lt;/a&gt;
&lt;/p&gt;</description></item></channel></rss>