The most depressing statistic in ML: 80% of models never make it to production.

I've seen this firsthand. A brilliant data scientist spends 3 months building a model with 94% accuracy. Everyone's excited. Then it sits in a notebook for 6 months because nobody knows how to deploy it.

Eventually someone manually exports it, wraps it in a Flask endpoint, and prays.

This is why I became obsessed with MLOps. Not because it's glamorous (it really isn't), but because the gap between "model works" and "model is working in production, being monitored, automatically retraining, and actually making money" — that gap is where careers are made.

If you're a data scientist reading this: learn Docker. Learn MLflow. Understand CI/CD for ML. Even just the basics.

The person who can build the model AND get it to production? That's not just an ML engineer. That's someone who's genuinely hard to replace.