ML System Design
You are given an uncertain research-heavy ML project that eventually must be shipped as a working артефакт. How do you decompose the work and communicate progress?
Ответить самому
Сначала сформулируйте ответ как на собеседовании, затем откройте разбор и оцените себя.
Короткий ответ
Decompose into deterministic milestones first: data and eval setup, runnable baseline, reproducible experiments, artifact packaging and rollout checks. Do not commit early to unreachable metric targets.
Полный разбор
For uncertain research, the first goal is to reduce unknowns while producing concrete artifacts. Start by clarifying the product objective, constraints, available data, evaluation protocol and what "usable" means. Then create milestones that are under engineering control: collect or freeze a dataset, build the metric harness, reproduce a known baseline, make one model run end to end, package inference, and prepare failure analysis.
Metric targets can be aspirational, but early commitments should be about evidence-producing steps. For example, commit to "we will run baseline X on dataset Y and produce slice metrics and examples" rather than "we will hit 99.9%". After the baseline, decide whether to continue, pivot or narrow scope based on measured gaps.
Progress communication should show artifacts: experiment tables, model cards, qualitative examples, bad-case buckets, ablations, cost/latency numbers and a clear next decision. This keeps stakeholders informed without pretending research is deterministic.
Теория
Research planning works best when uncertain quality targets are separated from deterministic learning milestones.
Типичные ошибки
- Commit to a final metric before knowing baseline feasibility.
- Spend weeks chasing papers before building the eval harness.
- Report only activity instead of evidence and decisions.
Как отвечать на собеседовании
- Use the phrase runnable baseline early.
- Name both technical artifacts and stakeholder-visible artifacts.