How would you reduce training cost without harming reproducibility?
PICTURE THIS: 1, 2, 2, 8
Simple meaning
Spot or preemptible GPUs with checkpointing, dataset sampling for iteration, cached DVC stages, and deleting failed-run artifacts while keeping successful hashes.
WHY — Cost instead of guessing?
Why interviewers care about Cost:
question about Cost.
trade-offs, and what you would actually do on a MLOps project - not buzzwords.
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:
- 1Spot or preemptible GPUs
with checkpointing, dataset sampling for iteration, cached DVC stages, and deleting failed-run artifacts while keeping successful hashes.
- 2Pin images so cheaper
hardware does not mean a different CUDA stack.
- 3Track cost per useful
run in the tracker.
- 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
Spot or preemptible GPUs with checkpointing, dataset sampling for iteration, cached DVC stages, and deleting failed-run artifacts while keeping successful hashes. Pin images so cheaper hardware does not mean a different CUDA stack.