Weekend Project: I Built a Full MLOps Pipeline for a Credit Scoring Model (And You Can Too)

A hands-on, beginner-friendly guide to deploying, monitoring, and optimizing a Machine Learning model in production — from API creation to data drift detection. Last Saturday morning, I was scrolling through freelance gig postings on Fiverr when I stumbled upon something that caught my attention. A small fintech startup was looking for someone to "take our trained credit scoring model and make it production-ready — API, Docker, CI/CD, the whole shebang." The budget was decent, the deadline was two weeks, and I thought: "How hard can it be?" Spoiler: it was more involved than I expected. But by Sunday evening, I had a working end-to-end MLOps pipeline running, and I learned an incredible amount in the process. This article is the tutorial I wish I had before starting. Whether you're a data science student, a junior ML engineer, or someone curious about what happens after a model is trained, this guide will walk you through every step — from serving predictions through an API to catching