A Quick Guide 
Model Selection
There is no single best AI model — only models that are well-matched to your data and your
question. Here's how to orient yourself.
your data lives in rows and columns — assay results,
instrument logs, sample metadata, experimental
If
histology, etc. — you’ll
need models that learn
from spatial patterns.
These models can process
images at a scale and
consistency no manual
review process can match.
If your data is
visual
conditions — you're working in the most well-supported
territory in applied machine learning. What you’re doing with it
will dictate your model:
Predict a number? Yield, concentration, degradation rate,
time to failure. Reliable tools exist for this that work well
even on modest datasets.
Classify into categories? Pass/fail, hit/non-hit,
stable/unstable. Complexity scales with how many
variables are involved and how much data you have.
Find what doesn't belong? Anomaly detection tools learn
what normal looks like from your historical data and
surface deviations automatically.
Forecast over time? Reagent consumption, stability
trends, demand planning. A distinct family of tools is built
specifically for data where sequence and timing matter.
 — plate imaging,
regulatory documents, etc.
— large language models
can be adapted to your
domain for summarization,
extraction, and structuring
of unstructured
information.
If your data is
text  — lab notes,
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