Design a DVC-plus-Git workflow for a dataset that updates daily and a model that retrains weekly.
PICTURE THIS: DATA SPLIT
Fit on train, tune on val, report on test once.
Simple meaning
Daily data stages update .dvc pointers on a data branch or via pipeline outputs in object storage tagged by date.
WHY — DVC instead of guessing?
Why interviewers care about DVC:
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:
- 1Daily data stages update
.dvc pointers on a data branch or via pipeline outputs in object storage tagged by date.
- 2Weekly training pins a
data snapshot hash in the training config and logs it to the registry.
- 3You never train on
a moving latest without recording the pointer.
- 4Give an example
One tiny concrete case you can say aloud.
- 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
Daily data stages update .dvc pointers on a data branch or via pipeline outputs in object storage tagged by date. Weekly training pins a data snapshot hash in the training config and logs it to the registry.