https://yvesdenis.github.io/tags/mlops/Recent content in MLOps on Time-to-GeekHugoen-usSun, 31 May 2026 22:13:51 -0400https://yvesdenis.github.io/post/retail-churn-prediction-post/Sun, 31 May 2026 10:00:00 -0500https://yvesdenis.github.io/post/retail-churn-prediction-post/<h2 id="introduction-">Introduction 🧠</h2> <p>You&rsquo;ve trained a model. It works great on your laptop. You ship it. Six months later, nobody&rsquo;s maintained it, the predictions are garbage, and your data scientist has moved on. Sound familiar?</p> <p><img src="https://yvesdenis.github.io/images/memes/this-is-fine.jpg" alt="This is fine — ML model on fire with no monitoring"></p> <p><em>Every ML team at some point. Don&rsquo;t be this dog. 🐶🔥</em></p> <p>That&rsquo;s exactly the problem this project tackles — head on, in three progressive phases. We&rsquo;re building a <strong>customer churn prediction system for retail</strong>, starting from a messy Jupyter notebook and ending with an <strong>autonomous AI agent</strong> that monitors drift and retrains the model without you lifting a finger.</p>