What is an artifact store in MLOps?
PICTURE THIS: 1, 2, 2, 8
Simple meaning
An artifact store is object storage for models, plots, datasets, and pipeline outputs, addressed by run ID or hash.
WHY — Experiment Tracking instead of guessing?
Why interviewers care about Experiment Tracking:
people 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:
- 1An artifact store is
object storage for models, plots, datasets, and pipeline outputs, addressed by run ID or hash.
- 2Why it exists
The tracker database stores metadata
- 3How it works
the store holds the bytes.
- 4Losing the store means
you cannot redeploy that run.
- 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
An artifact store is object storage for models, plots, datasets, and pipeline outputs, addressed by run ID or hash. The tracker database stores metadata