ML Fundamentals for MLOps
Introduction
You don't need to be a data scientist to do MLOps, but you need to understand enough
ML concepts to operate ML systems effectively. This section covers the vocabulary and
concepts that come up constantly in MLOps work.
Why This Matters
Imagine deploying a model without understanding what "overfitting" means — you wouldn't
know why the model performs great on test data but terribly in production.
Or setting up model monitoring without understanding "data drift" — you wouldn't know
what you're monitoring for.
MLOps engineers work alongside data scientists. You need enough ML literacy to:
- Understand what the model does and why
- Set up appropriate training infrastructure
- Configure meaningful monitoring and alerts
- Debug production issues that might be model-related