Data Collection and Annotation

1
THE CHALLENGES OF Data Collection and
Annotation A lot of training data is required to
develop an ML/AI model that functions as a
human. The model must be trained to recognize
specific objects to make decisions and take
action. It is important to categorize and label
the datasets for each use case properly.
High-quality, human-powered annotation services
can be used to enhance M L/AI algorithm
1.
Managing large workforce ML and AI models require
a lot of data to learn.
2.
Cutting-edge technologies It takes a lot of
skilled and experienced people to create
high-quality, labeled data sets.
3.
Falling short on consistent An accurate D ata
annotation platform demands high-quality data
tagging
4.
Not a cheap affair Annotating data is a tedious
process.
Although it may seem appealing to use your data
annotation services, it is not the best way to
grow in the future.
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