ML Experiment Architect

ML Experiment Architect: practical plan

Experiment plan

Hypothesis and baseline

State the expected improvement and target user outcome. Preserve the current ranking system as the baseline. Define offline and online success metrics before looking at results.

Data protocol

Use time-aware or entity-aware splits appropriate to deployment. Prevent user or item leakage across train and test. Document feature availability, exclusions and label delays.

Metrics

Use ranking metrics such as NDCG@k or Recall@k alongside latency, coverage, diversity, complaints and downstream task completion. Select metrics that match the product goal.

Reproducibility

Version data snapshots, code, features, model parameters, random seeds, environment and evaluation set. Log experiments with traceable artifacts.

Online test

If offline results justify it, run a controlled, privacy-aware experiment with guardrails and sufficient duration. Monitor sample balance, novelty effects and segment outcomes.

Decision

Ship only if gains are meaningful, stable and do not harm guardrails. Record uncertainty, limitations, rollback triggers and monitoring ownership.

Measurement and review

Record the baseline, source, date range, denominator and owner. Use one primary measure plus quality guardrails; compare like-for-like cohorts and disclose missing data. Treat estimates as scenarios, not observed outcomes.

Risks and controls

Check relevance, consent, disclosure, workload and downside before scaling. Keep an evidence trail, obtain human review for material commitments, and pause if trust, quality or compliance signals deteriorate.

Next steps

1. Confirm the goal, audience and available capacity.
2. Gather the minimum evidence for a first test.
3. Assign an owner, budget and review date.
4. Run a small pilot, record outcomes and decide whether to iterate, expand or stop.