A limited dataset is a starting point, not a dead end. Synthetic data is
a legitimate and widely used technique for getting models off the
ground when real-world data is incomplete or impossible to gather
(Think: rare diseases). Used carefully, it can be the difference between
a project that starts and one that doesn't.
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When Real Data is Limited
Synthetic Data Can Help
teams encounter when starting an AI
project. Sometimes the data doesn't exist
in large quantities. More often, it exists
but is fragmented across systems, is
inconsistently formatted, etc. A dataset
that looks substantial on paper can turn
out to be less so once you account for
missing fields, duplicate records, and
inconsistencies.
Synthetic data offers a principled way to
work around these constraints. In most
cases, you can responsibly generate
examples that reflect the properties of
real data closely enough to be useful.
C
ollecting sufficient high-quality data is
one of the most common obstacles
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