Most MLOps content focuses on tools—trackers, containers, orchestration. Useful, but not where teams lose the most time. The real pain comes from skipping “optional” practices that become mandatory once the model is live. 1) Shipping a model with no monitoring Models don’t fail like software. APIs throw 500s.
Everything between "the model works on my machine" and "the model works in production, reliably, forever." You've trained a model with solid metrics, wrapped it in a FastAPI endpoint, and deployed it to a container. Congratulations — you've crossed the first threshold. But
When you start using PyTorch, it quickly feels like you’re fighting the framework: * Shapes don’t match * The loss doesn’t move * The GPU complains * Error messages look cryptic The good news: 80% of the problems come from the same few causes. If you get a few simple habits