Agile in ML borrows the iterative, value-driven mindset of software sprints but adapts for uncertainty, experiments, and data-dependencies.
Sprint planning & backlog grooming:
- Break work into small, deliverable slices: data ingestion, feature engineering, baseline model, infra, evaluation dashboards.
- Prioritize by risk and value (RICE): reduce high-risk unknowns first (data quality, label gaps).
- Groom backlog with explicit items for experiments, infra, and productionization; add acceptance criteria and estimated uncertainty.
Handling research spikes:
- Treat spikes as time-boxed discovery tasks with clear questions, success criteria, and deliverables (not “research forever”).
- Output of a spike = reproducible notebook, metrics, failure modes, and recommended next steps (pass/fail).
- Limit spikes per sprint and pair researchers with engineers to ease knowledge transfer.
Definition of Done for experiments:
- Reproducible training run with seed and environment spec
- Evaluation on held-out data with agreed metrics
- Documented failure cases and next steps
- Code reviewed, tests for data pipeline, and baseline comparison recorded
- If intended for production: model artifact pushed to registry and CI checks passed
Incorporating model validation into sprint work:
- Include validation tasks as first-class backlog items: unit tests for feature transforms, data validation (schema, drift), model validation (cross-val, calibration), and performance/regression tests.
- Automate validation in CI: sanity checks, reproducible metrics, and canary/A-B test plans for rollout.
- Make monitoring and post-deployment metrics (latency, accuracy, data drift) part of the acceptance criteria.
Overall: keep iterations short, surface uncertainty early, make experiments measurable and reproducible, and treat production hardening (infra, testing, monitoring) as non-negotiable sprint work.
