AI, Modeling & Data Science
Models that learn the right thing and prove it.
Use this for
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You’ve got features but no defensible model or evaluation harness.
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Performance shifts between lab ↔ field or across cohorts/devices.
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Stakeholders ask for calibration, bias, and monitoring (not just AUC).
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Notebooks don’t match production; reproducibility is shaky.
What you walk away with
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Modeling plan — target, cohorts, costs/latency, fairness and failure modes.
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Evaluation harness — leakage guards, true splits, calibration & bias metrics.
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Reproducible pipelines — versioned data/code (DVC/MLflow), CI tests.
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Model cards & docs — scope, lineage, limits, release gates.
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Deployment-ready artifacts — scored datasets, API/SDK stubs, dashboards.
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Monitoring plan — drift signals, thresholds, alerts, and rollback paths.
Patterns we reach for
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Leakage-proofing first — temporal & subject splits, cohort stratification.
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Calibration always — ECE/BSR, reliability diagrams, threshold setting.
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Bias audits — segment metrics, impact framing, guardrails.
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Baselines that beat hype; ablations to earn every feature.
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Design for change — dataset & model versioning, immutable lineage.
Quality gates
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Validity: holdout or CV with explicit leakage tests.
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Calibration: ECE within target; uncertainty surfaced in UI/API.
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Robustness: sensitivity to motion/missingness/device drift measured & budgeted.
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Governance: datasets/models versioned; runs reproducible end to end.
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Ops: p95 latency/cost within budget; monitoring thresholds defined.
Rapid · 2–3 weeks
Architecture + Eval Harness
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Modeling plan, splits, baseline models, calibration & bias report.
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Clickable demo and “ship/no-ship” memo.
Build · 6-8 Weeks
Integrated Pilot
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Pipelines with DVC/MLflow, model card, dashboards, API/SDK stubs.
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Robustness & ablations; release gates wired.
Oversight (Monthly)
Monitoring & Drift
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Metric & data-drift checks, fairness watch, threshold reviews, rollback notes.
Example runs
DPIA + consent for EMA + passive sensing app (home use).
Depression-risk screening model with cohort bias audit & explanation surfaces.
Activity/stress detection from wearables with latency budget and drift monitors.
Literature extraction assistant with Retrieve → Verify → Decide evaluation.
Boundaries
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We won’t ship an AUC built on leakage or cherry-picked splits.
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If generalization fails, we show why and propose design/data fixes.
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Privacy wins: PII boundaries and retention are part of the design.
Turn ideas into results that travel.
Book a 15-minute free consultation or ask for a sample design pack.
FAQ
Can you work with small datasets?
Yes—regularization, shrinkage, hierarchical models, uncertainty reporting; sometimes better data beats tuning.
Is real-time feasible?
We budget latency vs. accuracy and show the tradeoff before rollout.
Which stack?
Python-first (scikit-learn, XGBoost, PyTorch/Lightning), MNE/NeuroKit for signals, DVC/MLflow for lineage.
Do you handle fairness?
We define relevant cohorts, measure impacts, and set guardrails with decision-makers.
Will you deploy for us?
We package artifacts and pipelines but deployment is not something we do. We can pair with your engineers.
Need Some Help?
Feel free to contact us for any inquiry or book a free consultation.
