Situation: Ambiguous requirements caused wasted experiments and misaligned models. To prevent this, I use a structured ML Requirement Specification & Review template that forces clarity on business goals, data, metrics, constraints, and approvals.
Template — Key fields (with purpose and examples):
- Title & Summary
- One-line objective and short description of expected ML capability.
- Example: "Personalized email ranking to increase click-through rate (CTR) for re-engagement campaign."
- Business Objective & Success Criteria
- Business KPI to impact, baseline, target, and timeline.
- Example: "Lift CTR from 3.0% → 3.6% (+20%) within 8 weeks; ROI threshold: cost per incremental click < $2."
- Problem Statement & Scope
- What’s in/out of scope; user segments; failure modes acceptable or not.
- Example: "Only active users (last 90 days). No changes to transactional emails."
- Stakeholders & Roles (with signoffs)
- Product owner, data owner, ML lead, infra/engineering, compliance, QA; required approvals and signoff order.
- Data Specification
- Data sources, schemas, sample sizes, freshness, labeling process, known biases, required lineage and access controls.
- Example: "Training: transaction_logs v2, 6 months, 10M rows."
- Success Metrics & Evaluation Plan
- Primary metric (business-facing), secondary metrics (accuracy, fairness, latency, calibration), evaluation datasets, statistical significance thresholds, and monitoring plan.
- Example: "Primary: CTR lift measured via randomized A/B; Secondary: ROC-AUC ≥ 0.78; Latency ≤ 150ms P50."
- Experiment Design & Validation
- Train/val/test splits, cross-validation strategy, offline vs online evaluation, ablation studies, significance test (e.g., t-test, bootstrap), minimum detectable effect (MDE).
- Example: "MDE = 0.4% absolute CTR at 80% power."
- Constraints & Non-Functional Requirements
- Latency, throughput, memory, model size, deployment windows, regulatory/compliance constraints, explainability needs.
- Success/Failure Criteria & Rollback Plan
- Concrete pass/fail rules for go/no-go and rollback steps if metrics regress in production.
- Logging, Monitoring & Alerting
- What to log, drift detection metrics, SLA for alerts, dashboards, retraining triggers.
- Security, Privacy & Compliance
- PII handling, retention, consent, anonymization, data retention policy.
- Deliverables & Timeline
- Artifacts: dataset snapshot, training code, model card, deployment config, test reports, expected milestones.
- Trade-offs & Alternatives Considered
- Short justification for chosen approach and backup plans.
- Postmortem & Learnings
- Template field to capture outcomes, variance from expectations, and next steps.
Why this reduces ambiguity:
- Links technical choices to business KPIs and concrete statistical thresholds.
- Forces early signoffs from data and product owners to prevent downstream rework.
- Specifies experiment design and MDE so engineers know required sample sizes before training.
- Includes monitoring and rollback to limit production risk.
Signoff process:
- Sequential signoff: Product → Data Owner → ML Lead → Infra/Engineering → Compliance → Final Go/No-Go by Product + ML Lead.
Use this template in an initial kickoff doc and gate it at specification review; require all signoffs before expensive experiments or production deployment.
