Model Choice Tailored to Data & Intended Use
GMLP Principle 6. Pick model architectures grounded in your data realities and intended use, and document the risks you are actively managing.
Overview
Model architecture choices made for engineering convenience or recency bias often collapse under regulatory scrutiny. The right architecture balances data scale, intended use complexity, deployment constraints, and risk surface, not just benchmark performance.
We help make and defend that choice.
Our Process
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1
Constraint inventory
Data scale, latency, interpretability, security needs.
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2
Architecture candidate slate
Three to five candidates with documented trade-offs.
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3
Risk inventory
Per candidate: overfitting, drift, adversarial, supply chain.
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4
Comparative evaluation
Empirical comparison on representative data.
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5
Documentation
Choice rationale defensible to FDA and Notified Body.
Frequently Asked Questions
Should we use a foundation model?
Depends on data scale, intended use, and your tolerance for supply chain risk.
How do you handle adversarial robustness?
Treated as first-class risk, especially for safety-relevant predictions.
Interpretability trade-offs?
Surfaced explicitly; sometimes interpretability outweighs marginal performance gains.
Can we change architecture later?
Yes, with change control. But changes mid-program are expensive.
Pick the model you can defend.
Send us your current architecture and constraints. We will return an architecture review within four weeks.
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