Every meaningful change to your model — new training data, adjusted
parameters, architectural updates — should be logged and reversible. This isn't
just good engineering practice; in regulated environments it's the foundation of
your audit trail. If a model's behavior changes and you can't explain why, that's a
compliance problem as much as a technical one.
Track and version everything
Treat your model like critical lab equipment
You wouldn't run an instrument without calibration records, maintenance logs,
and a defined procedure for what happens when it drifts out of spec. A production
AI model deserves the same discipline. That means knowing exactly what version
is running, what data it was trained on, and what its performance looked like at
baseline — so you can detect when something has changed.
A model without a workflow is not a product
A model that produces output no one can act on directly is not a usable tool — it's
an experiment. For a model to become part of how your lab operates, its outputs
need to land somewhere familiar: inside your LIMS, your ELN, your review
workflow. Integrating your model with internal systems can be a hefty technical
job if you lack the expertise..
Validate outputs before they influence decisions
Before a model touches production workflows, its behavior should be tested
systematically against known examples — including edge cases and historical
failures. You should know how it performs, where it's confident, and where it
isn't. That knowledge doesn't come from a single test run; it comes from a
structured validation process that your team defines in advance.
Plan for the model to change
A model trained on today's data will gradually become less accurate as processes
evolve, reagent lots change, and new patterns emerge. Build in a regular review of
model performance against defined benchmarks, and establish the conditions that
would trigger a retraining cycle. 
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