Research & Analysis Checklist for AI & Machine Learning

Write questions that tie directly to business outcomes, for example reduce false escalations by 15 percent or cut serving cost per request by 20 percent. State clear, testable hypotheses with expected effect sizes so analysis plans are anchored to measurable impact.

Choose task-appropriate metrics like AUROC, F1, MRR, win rate, perplexity, or token cost, and document the current baseline and minimum detectable effect. Define upfront stop rules for futility or success to avoid open-ended, low-value iteration.

Capture GPU hour budgets, dataset access permissions, regulatory boundaries like SOC 2 or HIPAA, and delivery dates in a one-page brief. Constraints inform feasible design choices and prevent midstream rework.

List PM, legal, data engineering, and security reviewers with response SLAs, and name one decision owner for tradeoffs. This reduces approval churn and clarifies who signs off at each stage.

Document statistical tests, evaluation splits, ablation plans, and selection criteria before touching data. Pre-registration curbs p-hacking and makes final claims credible to leadership and auditors.

Track risks such as data drift, annotator inconsistency, vendor rate-limit changes, and model regressions, and assign owners with mitigation steps. Review weekly so risks are actively managed rather than discovered in post-mortems.

Identify relevant tables, S3 buckets, and feature store entities with freshness, schema, and ownership tags. Include edge-case coverage and sampling frames to ensure the evaluation set represents real failure modes.

Assess representativeness, update cadence, and legal terms like training versus inference rights and redistribution clauses. Record API quotas, latency SLAs, and content policy restrictions to avoid later production blockers.

Use Great Expectations or Deequ for schema, range, and completeness tests, and Evidently for drift checks across candidate splits. Block experiments on failing checks so you do not optimize against corrupted inputs.

Use DVC or LakeFS to pin dataset hashes and link them to Git commits, and leverage Delta Lake or Apache Iceberg time travel for rollback. This enables exact reproduction of past experiments and reliable diffing.

Scan sources and outputs with Microsoft Presidio or custom detectors, then apply anonymization or synthetic data where required. Document DPIAs and retention windows to satisfy legal and customer commitments.

Build Airflow or Dagster jobs with dbt transformations that materialize training, validation, and evaluation slices on a schedule. Parameterize by date and cohort to support backtesting and controlled rollbacks.

Use ArXiv Sanity, Papers With Code alerts, and Semantic Scholar feeds for task and dataset keywords. Triage weekly and tag papers by dataset and metric so shifts in state of the art are visible at a glance.

Snapshot SOTA tables from Papers With Code and replicate small-scale baselines using author repos or open equivalents. Note preprocessing, augmentation, and hardware details that materially influence reported gains.

Watch GitHub organizations for releases and commit patterns, and diff model cards across versions. Tracking velocity and direction reveals technical bets and informs your own roadmap priorities.

Maintain a registry of provider prices, token limits, rate limits, and content policies with change logs. Recompute unit economics and throughput projections whenever terms shift so recommendations stay grounded.

Collect SOC 2, ISO 27001, HIPAA attestations, and red-teaming notes, and cross-check evaluation claims against datasets and metrics used. This strengthens due diligence and vendor comparisons.

Use Zotero, Notion, or Obsidian with templates that capture summary, dataset, metric, code links, and threats to validity, and link to replication notebooks. A curated library speeds synthesis for briefings and reviews.

Adopt cookiecutter-data-science or a similar scaffold with dedicated folders for configs, data, models, and reports, and enforce experiment YAML. Consistent structure accelerates discovery and reliable comparisons.

Pin Python, CUDA, and library versions in Docker and conda, and lock RNG seeds for NumPy, PyTorch, and any LLM sampling parameters like temperature and top_p. Record GPU type and driver so runs are replayable.

Use MLflow or Weights & Biases to log dataset hashes, code commits, hyperparameters, metrics, and artifacts with required tags. Enforce schemas so cross-project queries and dashboards remain consistent.

Use Optuna or Ray Tune with ASHA or Bayesian strategies, and encode ablations as explicit flags. Persist search spaces in config so you can replay or compare across branches and time.

Use promptfoo or LangSmith to run deterministic prompt suites with fixed seeds and golden outputs, and unit test evaluation code to prevent metric drift. Gate merges on passing regression checks in CI.

Apply group-wise or time-based splits as appropriate, and run leakage scanners to block label bleed and duplicated examples. Document split logic so cross-experiment comparisons are fair and stable.

Provide a CLI and Docker entrypoint that accepts a config ID, loads the matching artifacts, and writes eval JSON locally or in CI. This trims glue code and makes comparisons truly apples to apples.

Select metrics aligned to user impact, such as expected calibration error for classifiers, Rouge or BLEU with bootstrap CIs for generation, or win rate for LLM A/B tests. Report uncertainty so decisions do not overfit noise.

Include trivial baselines like majority class, logistic regression, zero-shot prompts, or small distilled models, and document ablations. This prevents illusory progress and clarifies where complexity pays off.

Combine aggregate metrics with slice analysis across cohorts, geographies, languages, or product segments using Fairlearn or custom notebooks. Automate runs for each candidate to ensure coverage is consistent.

Run perturbation tests like synonym swaps, noise injection, and truncation, and red-team LLMs using the OWASP Top 10 for LLM applications and MITRE ATLAS scenarios. Document reproducible failure cases and severity.

Use AIF360 or Fairlearn to evaluate disparate impact, equalized odds, and calibration across protected groups, and review domain-specific harms. Plan mitigations such as reweighting, thresholds, or post-processing if gaps are material.

Probe for PII leakage, prompt injection, and model inversion risks, and confirm logs do not over-collect sensitive data. Coordinate with security for threat models and tabletop exercises covering likely abuse paths.

Use Locust or k6 to simulate expected QPS and payloads, and compute cost per task across parameter choices, caching, or batching strategies. Apply SLOs and cut candidates that cannot hit budget or responsiveness targets.

Produce datasheets for datasets and model cards that capture intended use, training data, caveats, and evaluation results following the Model Cards framework by Mitchell et al. Standardized documentation accelerates audits and partner reviews.

Write a concise brief with a one-page executive summary, key charts, and metric tables, including power analysis and uncertainty. Emphasize decision criteria, tradeoffs, and recommendations rather than exhaustive logs.

Record every change to data, code, and configuration in a human-readable changelog linked to tracking runs. This speeds root cause analysis when regressions appear and supports audits.

Use Apache Superset, Metabase, or Grafana to expose evaluation metrics, cost, and latency trendlines with slice filters for key cohorts. Stakeholders can self-serve insights without bespoke notebooks.

Assemble evaluation reports, compliance artifacts, and risk assessments into a shareable packet with reproducible notebooks and redacted datasets. This shortens procurement cycles and external validation.

Store models, datasets, notebooks, and reports in versioned buckets with lifecycle policies and role-based access, and keep approvals and sign-offs alongside. Clean exits and audits depend on disciplined archiving.