Blanked for a second because I came in expecting roadmap or metrics questions, not something this deep in the ML weeds.
Frame data curation as a product strategy problem: start by defining the model's target capabilities and quality bar, then design a scalable pipeline that balances data quality, diversity, and cost. Emphasize iterative feedback loops and measurable metrics to guide trade-offs, and tie your approach to NVIDIA's ecosystem and enterprise needs.
Pro tip: Show you understand that data curation is not a one-time task but a continuous product lifecycle: mention how you'd instrument the pipeline to detect drift and re-curate as model requirements evolve. Also, reference NVIDIA's specific tools (e.g., NeMo Curator, RAPIDS) to demonstrate domain awareness.
Clarify the model's intended use cases, performance targets, and constraints (e.g., latency, cost, compliance). Translate these into measurable data quality dimensions like relevance, diversity, and toxicity thresholds.
Outline stages: data acquisition, cleaning, deduplication, filtering, annotation, and augmentation. Choose techniques (e.g., heuristic rules, model-based scoring) and tools (e.g., NeMo Curator) appropriate for scale and modality.
Balance quality vs. quantity, cost vs. performance, and speed vs. thoroughness. Use a tiered approach: high-quality curated data for critical capabilities, broader data for generalization, and synthetic data to fill gaps.
Define metrics (e.g., data coverage, label accuracy, model eval scores) and set up monitoring to detect data drift or degradation. Iterate based on model performance and user feedback.
Plan for distributed processing, versioning, and governance. Ensure the pipeline is reproducible, auditable, and adaptable to new data sources or regulatory changes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.