<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Week 1 - Advanced DevOps on AI Platform Engineering Handbook</title><link>/docs/week-01/</link><description>Recent content in Week 1 - Advanced DevOps on AI Platform Engineering Handbook</description><generator>Hugo</generator><language>en</language><copyright>Copyright (c) 2026 Harshhaa</copyright><atom:link href="/docs/week-01/index.xml" rel="self" type="application/rss+xml"/><item><title>Advanced Git &amp; CI/CD Engineering</title><link>/docs/week-01/advanced-git-cicd-engineering/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>/docs/week-01/advanced-git-cicd-engineering/</guid><description>&lt;hr&gt;
&lt;h2 id="advanced-git-engineering"&gt;ADVANCED GIT ENGINEERING&lt;/h2&gt;
&lt;hr&gt;
&lt;h2 id="1-git-internals--object-model"&gt;1. Git Internals &amp;amp; Object Model&lt;/h2&gt;
&lt;p&gt;Most engineers use Git every day without understanding how it actually works internally. This is fine for basic usage, but when things go wrong — a rebase goes sideways, a reset loses commits, a merge produces unexpected results — understanding Git&amp;rsquo;s internals is the difference between recovering confidently and panicking. More importantly, once you understand the object model, everything Git does makes intuitive sense.&lt;/p&gt;</description></item><item><title>Docker &amp; Container Security Engineering</title><link>/docs/week-01/docker-container-security-engineering/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>/docs/week-01/docker-container-security-engineering/</guid><description>&lt;hr&gt;
&lt;h2 id="1-containerization-fundamentals"&gt;1. Containerization Fundamentals&lt;/h2&gt;
&lt;p&gt;Before containers existed, deploying software was genuinely painful in ways that are hard to appreciate if you haven&amp;rsquo;t lived through it. You&amp;rsquo;d write an application on your laptop, running Python 3.9 with specific library versions on macOS. The staging server runs Python 3.7 on Ubuntu. Production runs Python 3.8 on CentOS. Each environment has different library versions installed, different system dependencies, different file paths, different environment variables. Your application works perfectly on your laptop and mysteriously breaks in production. The phrase &amp;ldquo;it works on my machine&amp;rdquo; became a running joke in the industry because it was so universally true and so universally frustrating.&lt;/p&gt;</description></item><item><title>Kubernetes Production Concepts</title><link>/docs/week-01/kubernetes-production-concepts/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>/docs/week-01/kubernetes-production-concepts/</guid><description>&lt;hr&gt;
&lt;h2 id="1-kubernetes-architecture-overview"&gt;1. Kubernetes Architecture Overview&lt;/h2&gt;
&lt;p&gt;Before diving into individual components, you need a mental model of what Kubernetes actually is and why it exists, because everything else makes more sense with that foundation.&lt;/p&gt;</description></item><item><title>Observability &amp; Monitoring</title><link>/docs/week-01/observability-monitoring/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>/docs/week-01/observability-monitoring/</guid><description>&lt;hr&gt;
&lt;h2 id="1-observability-principles"&gt;1. Observability Principles&lt;/h2&gt;
&lt;p&gt;Before anything else, let&amp;rsquo;s separate two concepts that are often conflated: monitoring and observability. They&amp;rsquo;re related but fundamentally different in philosophy, and understanding the difference shapes how you build systems.&lt;/p&gt;</description></item></channel></rss>