How to deploy, monitor, and optimize ML models in production: GPU selection, VRAM requirements, cloud vs local cost, model drift, and CI/CD pipelines for ML
The Model That Worked in Jupyter and Failed in Production
In 2020, a team at a financial services company trained a fraud detection model that achieved 99.2% accuracy in their Jupyter notebooks. They deployed it to production. Within a week, false positives had increased by 300%, and the company was blocking legitimate transactions from thousands of customers. The model wasn't broken — the data had changed. The pandemic had shifted spending patterns so dramatically that the model's training data no longer resembled reality.
This is the central lesson of MLOps: a model is not a feature you ship once. It's a system that degrades over time. Unlike traditional software, where a bug either exists or doesn't, ML models silently degrade as the world changes around them. The data drifts. The distribution shifts. The model that was accurate last month becomes inaccurate this month — and without monitoring, you won't know until customers complain.
MLOps (Machine Learning Operations) is the set of practices that keeps ML models working in production: deploying them reliably, monitoring them continuously, retraining them when they degrade, and doing all of this at scale. This guide covers the fundamentals: choosing the right hardware for inference, optimizing costs (cloud API vs self-hosting), detecting model and data drift, and building CI/CD pipelines that work for ML systems.
