Week-by-week, timeboxed iteration plan to deliver an MVP in 2 weeks that balances speed and quality:
Day 0 — Clarify scope & success metrics (half day)
- Define narrow use-case, acceptance metrics (precision/recall, business-impact proxy), and dataset sources.
- Target: a focused slice that captures 70–80% of expected traffic.
Days 1–2 — Select dataset subset (1.5 days)
- Sample stratified subset (2–5k examples) by key axes (user segment, class imbalance, edge cases).
- Prioritize high-impact & high-uncertainty buckets (top 3 error modes from logs or heuristic rules).
- Keep separate small holdout (10–15%) for final validation.
Days 2–5 — Fast labeling & labeling strategy (3.5 days)
- Combine weak supervision + rapid human labeling:
- Create 5–10 labeling rules/snippets (heuristics, regex, distant supervision) to auto-label ~30–50% quickly.
- Use active learning: train a cheap model on auto-labeled data, surface top-uncertain 500–1,000 samples for human labelers in batch.
- Run labeling in parallel with a small team or outsourced micro-tasks; provide clear label guide and examples.
- Expected labeled set by Day 5: 2–3k reasonably high-quality labels.
Days 5–10 — Lightweight modeling & iteration (5 days)
- Start with transfer learning / simple baselines for speed:
- For text: fine-tune a small transformer (DistilBERT) or use TF-IDF + logistic regression for baseline.
- For images: use pre-trained MobileNet + few-layer head.
- Iterate rapid cycles (1-day experiments): model → quick validation → error analysis.
- Use data augmentation and class rebalancing if needed.
Days 10–12 — Validation & risk assessment (2–3 days)
- Holdout evaluation, cross-validation, calibration checks, failure case audit on prioritized buckets.
- Run robustness tests (noisy inputs, edge cases), compute business metrics (false positive cost).
- Estimate uncertainty and degradation scenarios; prepare mitigation (human-in-loop fallback).
Days 12–14 — Demo & stakeholder plan (2 days)
- Demo components:
- Executive slide: problem, approach, key metrics vs acceptance criteria, risks/mitigations.
- Live demo: model inference on real-ish examples including edge cases and fallback behavior.
- Walkthrough of labeling pipeline and roadmap to production (data, monitoring, SLA).
- Provide tangible artifacts: notebook, runnable Dockerized inference, confusion matrices, error examples, and a clear next-steps backlog (scale labeling, model hardening, A/B test plan).
Why this balances speed & quality
- Narrow slice + stratified sampling ensures early wins on common cases.
- Weak supervision + active learning accelerates label acquisition while focusing human effort where it helps most.
- Lightweight transfer models give strong baseline performance quickly; simple models are interpretable for stakeholders.
- Early validation and human-in-loop strategies reduce deployment risk and provide measurable confidence for stakeholders.
