The cost of NOT doing MLOps

Why this matters

It's easy to treat MLOps practices as optional polish until you've lived through what their absence costs. Three failure stories recur across teams: a model silently decays for months because no one is watching accuracy, and by the time revenue drops the trail is cold; a model that made a bad decision can't be reproduced because the training data and seed were never captured, so you can neither explain nor fix it; and a bad deploy takes down quality with no way to roll back because the previous model artifact was overwritten. Each of these is not bad luck — it's a specific missing practice (monitoring, versioning, registries/rollback). Pricing out the failure is what justifies the work to a skeptical team, because 'we can't reproduce or revert our own model' is a far scarier sentence than 'we skipped experiment tracking.'

Demo

The demo maps three real failure modes to the exact MLOps practice that prevents each, and computes a crude cost of the gap so you can see why the practice pays for itself.

Try it yourself

1. Pick the one failure story most likely to hit your team and name the exact practice (from a later module) that closes it.
2. Change days_undetected on the monitoring story from 90 to the number of days YOUR system could decay unnoticed today.
3. Add a fourth failure your team has actually experienced and map it to a missing practice.
4. Total the avoidable cost and compare it to the effort of adding the one practice that prevents the biggest line item.

References

• Sculley et al. — Hidden Technical Debt in ML Systems
• Google — Why ML projects fail to reach production

Chat about this lesson