Machine learning for research — workflow template

Purpose

This template helps research teams run ML experiments and production prototypes with reproducible pipelines, clear validation, and responsible governance. It focuses on practical artifacts, checks, and roles so models produce useful, trustworthy, and repeatable results rather than opaque one-off outcomes.

High-level workflow

1. Data ingestion & contract definitions
2. Feature engineering & feature catalogue entry
3. Training pipeline, validation folds, and baseline comparisons
4. Interpretability checks and subgroup analyses
5. Model registry entry, deployment constraints, and monitoring plan

Expanded checklist & guidance (stage-by-stage)

1. Data ingestion & contract definitions

Goals: ensure datasets are well-defined, versioned, and auditable.

• Define a data contract: schema, required fields, units, expected distributions, privacy constraints, and update cadence.
• Record dataset provenance: sources, extraction queries, sampling method, time window, and any joins or cleaning steps.
• Version datasets (hashes, dataset IDs) and snapshot training/test partitions. Store a representative sample for reproducibility.
• Run basic data-quality checks: null rates, value ranges, class balance, duplicate records, and foreign-key integrity.
• Consider privacy and legal constraints (PII handling, consent, encryption, minimized retention).

2. Feature engineering & feature catalogue entry

Goals: make features discoverable, reusable, and testable.

• Document each feature: name, definition/formula, source fields, expected distribution, and transformation steps.
• Use a feature catalogue or registry (even a simple shared spreadsheet) with versioning and owners.
• Prefer deterministic, unit-tested feature transformations and store code alongside data pipelines.
• Create automated drift checks for key features (mean, std, missingness) and record alerts/thresholds.
• Tag features that could leak label information (temporal leakage) and check causal ordering.

3. Training pipeline, validation folds, and baseline comparisons

Goals: produce reliable performance estimates and avoid overfitting to the validation procedure.

• Design a validation strategy suited to the problem: time-based split for temporal data, stratified k-fold for class imbalance, nested CV for hyperparameter tuning.
• Keep a held-out test set that is untouched until final evaluation. Record its version/hash.
• Establish baseline models (simple heuristics, linear models, or prior published models) and require new models to outperform baselines on agreed metrics.
• Log hyperparameter search details, random seeds, optimization libraries, and compute environment (container image or env spec).
• Automate unit and integration tests for pipeline stages (data loading, feature transforms, training step producing expected output shapes and metrics ranges).

4. Interpretability checks and subgroup analyses

Goals: understand model behavior, identify fairness/performance gaps, and build trust.

• Compute global and local explanations (feature importances, SHAP, partial dependence) and save explanation artifacts with model metadata.
• Run subgroup performance analyses (by demographic, geography, device, time period) and report metric deltas vs. overall.
• Check calibration (reliability diagrams, Brier score) for probabilistic outputs and apply calibration methods if needed.
• Run counterfactual or sensitivity analyses for important features to probe causal plausibility and stability.
• Document limitations clearly (regions of low confidence, missing subgroups, extrapolation warnings).

5. Model registry entry, deployment constraints, and monitoring plan

Goals: capture model metadata, enforce safe deployment, and measure live performance.

• Register the model with metadata: model ID, version, git commit, training dataset hash, feature set version, training metrics, and owner/contact.
• Specify deployment constraints: allowed input ranges, expected throughput, latency targets, and safe-value/guardrail rules.
• Define monitoring metrics: input feature drift, prediction distribution drift, key business/clinical metrics, rate of invalid inputs, and downstream impact signals.
• Set alert thresholds and automatic rollback criteria (for example, drop in AUC > X or significant drift on key features).
• Plan regular re-training cadence or trigger-based retraining when drift is detected and log retraining decisions.

Reproducibility & governance artifacts to produce

• Data contract document and dataset snapshot IDs.
• Feature catalogue entries with owner and unit tests.
• Training pipeline code, environment spec (Dockerfile/Conda spec), and random seeds.
• Evaluation report comparing model vs. baselines and subgroup breakdowns (include figures and raw eval tables).
• Model registry entry with provenance and approval status.
• Monitoring & alerting configuration and runbook describing incident handling and rollback steps.

Roles & responsibilities (suggested)

• Research lead / PI: defines scientific requirements, approves experiments and interpretations.
• Data owner: maintains data contracts and approves dataset use.
• ML engineer / MLOps: builds pipelines, enforces CI/CD, and manages deployment & monitoring.
• Model steward: owner of model registry entry, responsible for model lifecycle decisions and approvals.
• Ethics / domain reviewer: reviews interpretability analyses and subgroup impacts for risks and fairness concerns.

Example minimal validation plan (copyable)

Common pitfalls and quick mitigations

• Data leakage: always check temporal ordering and exclude features derived from future data.
• Overfitting to validation: reserve a locked test set and minimize manual tuning on it.
• Unclear ownership: attach owners to datasets, features, and models to avoid orphaned assets.
• Poor monitoring: instrument for both data and model-level signals; plan runbooks before deployment.
• Opaque models in sensitive contexts: require interpretability and domain sign-off before any clinical/operational deployment.

Quick starter tasks

1. Create/record the data contract for your primary dataset and snapshot it.
2. Author three feature catalogue entries for your top features and add simple unit tests.
3. Run a baseline model and record metrics and artifacts in a single evaluation report.
4. Produce an interpretability report (SHAP or similar) for the baseline and candidate model.
5. Prepare a model registry entry template (metadata fields) to capture the provenance listed above.

Next steps & capability notes

This template is designed as a starting point. Teams may convert the reproducibility checklist into an interactive submission form (for pre-deployment gating), implement a feature registry, and connect model registry entries to monitoring dashboards.

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