This PDF copy is describing about the top four myths about outsource data annotation services with examples to clear unambiguous stories of data annotation outsourcing and what are the exact truth about such myths. The document is shared by the experts at Cogito encourgaong the AI and ML companies, why they should outsource their data annotation needs and get the better quality data for model training at affordable cost. Visit: https://bit.ly/2LSqtjy

1. Top Four Myths
About
Outsource Data
Annotation
Services

2. Getting Annotated data is not possible, only experts can do this
job better.
Clear The Myth From Your Mind And Read
Here The Top Four Myths About Outsourcing
Data Annotation With Few Suggestions To
Know How To Outsource The Data
Annotation.

3. Myth 1:
Is My Data is Safe
and Remains
Confidential with
Annotators?

4. This is one of the top reason,
companies try to annotate
the data at their in-house
resources. Actually, loosing
the data could be dangerous
for businesses, but its not
true, outsourcing companies
also operate professionally
and keep the clients data in
safe hands.
Myth 1:

5. Myth 2:
In-house Annotators
Can Do This Job Or
Don’t Need
Expertize

6. Myth 2:
This is also a hearsay, that
annotators are not skilled or not
specialized enough to annotate
with that quality, instead in-
house annotators can do this
job easily. Actually, nobody
knows better than you what is
your model, but it doesn’t mean
you can cater other aspects,
rather specialists can also
understand your requirements.

7. Myth 3:
You Think Your
Use Case is Too
Complicated
than others

8. Myth 3:
Many AI or ML companies
assume that their use case is
too much complicated, that
data annotation companies will
unable too meet the
requirements. But its not true,
having a great discussion with
both team members can easily
clear the exact requirements,
and annotators can deal with
any type of industry for
providing the training data.

9. Myth 4:
Hiring third-party
Annotators are
Expensive

10. Myth 4:
You have already spend
handsome amount of money on
machine learning engineer or
data scientist to develop a fully-
functional model for you.
Compromising with other inputs
will give you poor results. And
training a AI-enabled model
through computer vision is a
difficult job when you assign
such task to internal resources.

11. Thanks for
Watching