The Hidden Pitfall of Model Deployment: Why Monitoring Trumps Training
When I started working with Monitoring, I quickly realized there was a gap between theory and what actually happens in practice. This post is about why model monitoring matters more than model training. I'll walk you through what I learned, what tripped me up, and the lessons that stuck with me. No fluff — just honest notes from someone who went through it. Introduction to the Importance of Model Monitoring As I delved into the world of machine learning operations (MLOps), I quickly learned that the real challenge lies not in training a model, but in ensuring it continues to perform well after deployment. I've seen firsthand how a model that excels in testing can silently deteriorate in production, often due to data drift - a change in the distribution of input data that can render a model ineffective. My experiences have taught me that model monitoring is not just a nice-to-have, but a critical component of any successful MLOps strategy. The Dangers of Assuming Deployment ...