28:I[84576,["/_next/static/chunks/29ejg67v4rd5c.js","/_next/static/chunks/14mrh2-p_w84d.js","/_next/static/chunks/1uxsm7bia19wf.js","/_next/static/chunks/3d1xdvp9mxq8b.js","/_next/static/chunks/18kkvubgjxs0d.js"],"default"] 29:T4ce,

A production system should never overwrite an existing model.

Instead, I would:

Assign every model a version number.
Store model artifacts in versioned S3 folders.
Track metadata such as accuracy, training date, and dataset version.
Deploy new versions gradually.
Keep older versions available for rollback.
Monitor the new model before making it the default.

This makes deployments safer and easier to manage.

Why interviewers ask this Production AI systems continuously evolve, so model versioning is essential.

Common mistakes

Replacing the old model immediately.

Not tracking model versions.

No rollback strategy.

Follow-up questions

Why keep old models?

How would you compare two model versions?

What is model rollback?

Production insight: Successful AI teams treat models like software releases. Every model should be traceable, reproducible, and easy to roll back if problems appear.

AWS