Where should MLflow artifacts live relative to the tracking database?
PICTURE THIS: DATABASE INDEX
Simple meaning
The tracking DB stores metadata
WHY — MLflow instead of guessing?
Why interviewers care about MLflow:
who only read docs from people who shipped.
and tied to MLOps work.
Name the idea, why it exists, then one short example.
End with when you use it and one common pitfall.
STEPS — What happens step by step?
Before you speak the answer, walk the interviewer through these steps:
- 1Define it
The tracking DB stores metadata
- 2artifacts should live in
durable object storage with the same lifecycle policy.
- 3Putting artifacts on a
local tracking-server disk is a single point of failure.
- 4Back up the DB
and version-bucket the store.
- 5Common mistake
What juniors usually get wrong.
- 6Close
When you pick this over the alternative.
EXAMPLE — See it in action
Here's a short line you can speak, broken into clear beats:
Note: Adapt this scaffold to your own project — keep it under 60–90 seconds.
Key takeaway
The tracking DB stores metadata artifacts should live in durable object storage with the same lifecycle policy.