<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Pipelines on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/pipelines/</link><description>Recent content in Pipelines on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Fri, 20 Mar 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/pipelines/index.xml" rel="self" type="application/rss+xml"/><item><title>Chapter 5: Jenkins - The Enterprise Automation Hub</title><link>https://ai-blog.noorshomelab.dev/devops-journey-2026/jenkins-enterprise-automation/</link><pubDate>Mon, 12 Jan 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/devops-journey-2026/jenkins-enterprise-automation/</guid><description>&lt;h2 id="chapter-5-jenkins---the-enterprise-automation-hub"&gt;Chapter 5: Jenkins - The Enterprise Automation Hub&lt;/h2&gt;
&lt;h3 id="introduction"&gt;Introduction&lt;/h3&gt;
&lt;p&gt;Welcome back, future DevOps maestros! In our previous chapter, we explored GitHub Actions, a fantastic integrated CI/CD tool, especially for projects living on GitHub. Now, it&amp;rsquo;s time to meet another giant in the CI/CD landscape: &lt;strong&gt;Jenkins&lt;/strong&gt;. If GitHub Actions is like a sleek, modern sports car integrated tightly with its ecosystem, Jenkins is the powerful, highly customizable, and immensely flexible cargo ship that can be adapted for almost any journey.&lt;/p&gt;</description></item><item><title>Understanding Execution Pipelines and Request Routing in MCP</title><link>https://ai-blog.noorshomelab.dev/mcp-ai-tool-integration-guide/execution-pipelines-routing/</link><pubDate>Fri, 20 Mar 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/mcp-ai-tool-integration-guide/execution-pipelines-routing/</guid><description>&lt;h2 id="introduction"&gt;Introduction&lt;/h2&gt;
&lt;p&gt;Welcome back, intrepid AI architects! In our previous chapters, we&amp;rsquo;ve explored the foundational concepts of the Model Context Protocol (MCP), from its purpose as a universal language for AI tool interaction to the intricate details of defining and registering tools using robust JSON Schemas. You&amp;rsquo;ve learned how tools declare their capabilities, making them discoverable by AI agents.&lt;/p&gt;
&lt;p&gt;But how does an AI agent actually &lt;em&gt;use&lt;/em&gt; a tool once it&amp;rsquo;s discovered? How does a request travel from the agent, through the MCP system, to the correct tool, and then return a meaningful response? That&amp;rsquo;s precisely what we&amp;rsquo;ll unravel in this chapter: the fascinating world of &lt;strong&gt;Execution Pipelines&lt;/strong&gt; and &lt;strong&gt;Request Routing&lt;/strong&gt; within MCP.&lt;/p&gt;</description></item><item><title>Chapter 22: Deployment and CI/CD Pipelines</title><link>https://ai-blog.noorshomelab.dev/angular-production-guide-2026/deployment-ci-cd/</link><pubDate>Wed, 11 Feb 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/angular-production-guide-2026/deployment-ci-cd/</guid><description>&lt;h2 id="chapter-22-deployment-and-cicd-pipelines"&gt;Chapter 22: Deployment and CI/CD Pipelines&lt;/h2&gt;
&lt;p&gt;Welcome to Chapter 22! In our journey through building robust Angular applications, we&amp;rsquo;ve focused heavily on development, architecture, and testing. But what happens after your code is perfect and all tests pass? How does it get from your local machine to your users&amp;rsquo; browsers reliably and efficiently? This is where &lt;strong&gt;Deployment and CI/CD Pipelines&lt;/strong&gt; come in.&lt;/p&gt;
&lt;p&gt;This chapter will demystify the process of taking your production-ready Angular application and automating its delivery. We&amp;rsquo;ll explore Continuous Integration (CI) and Continuous Delivery/Deployment (CD) concepts, understanding why they are non-negotiable for modern software teams. You&amp;rsquo;ll learn about essential pipeline stages, how to optimize builds, implement safe release strategies like canary deployments, and ensure the security and observability of your deployed application.&lt;/p&gt;</description></item><item><title>Core Concepts: Pipelines and Models</title><link>https://ai-blog.noorshomelab.dev/transformers-js-guide/core-concepts-pipelines-and-models/</link><pubDate>Sun, 26 Oct 2025 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/transformers-js-guide/core-concepts-pipelines-and-models/</guid><description>&lt;h1 id="2-core-concepts-pipelines-and-models"&gt;2. Core Concepts: Pipelines and Models&lt;/h1&gt;
&lt;p&gt;In Transformers.js, the &lt;code&gt;pipeline&lt;/code&gt; function is your primary entry point for using pre-trained machine learning models. It abstracts away much of the complexity, allowing you to focus on the task at hand rather than the intricate details of model architecture, tokenization, or post-processing.&lt;/p&gt;
&lt;p&gt;This chapter will dive deep into understanding what pipelines are, how to use them, and the crucial role of models within these pipelines.&lt;/p&gt;</description></item></channel></rss>