1.
The Biggest
Myths about
Outsourcing
Data Annotation

2.
But even the smartest companies struggle to interpret it at scale.
High-quality data is the linchpin to
amazing AI models.

3.
3
Training Data for Computer Vision
• You need humans to interpret data with near-perfect
accuracy—especially in computer vision environments
like autonomous driving.
• Companies need accurate labeled datasets to train,
then continuously validate machine learning algorithms
and AIs.

4.
4
In-House Solutions Don’t Scale
• Many companies want to keep their data annotation
projects in house.
• But why?
• Because there’s a lot of myths and misconceptions
about third-party options…

5.
Myth 1:
“My data won’t
remain private or
secure

6.
6
Reality: Choose Trusted Partners Who Obsess about Security Protections
• Mighty AI customers can store data in secure locations within
their datacenters and give us temporary access that they
control.
• We can also store it in our own secure storage, where it’s
encrypted at rest.
• Authorized employees get to use the tooling, interface, and
other benefits of the Mighty AI platform.

7.
Myth 2:
“It’s too expensive
to hire a third-party
provider.”

8.
8
Reality: You’re Paying The Smartest People to Tedious, Unfulfilling Work
• Training AI models is tough when you’re relying on internal
resources. So bring in the experts.
• Mighty AI handles everything at a lower level of effort, higher
throughput, and fraction of the total time and cost of in-house
operations.
• You get UIs, workflows, tooling, project management,
targeting, training and qualifying our curated community of
Fives for tasks, quality assurance, testing, and validation.

9.
Myth 3:
“The annotators
aren’t skilled or
specialized
enough.”

10.
10
Reality: AIs are Only as Good as the Humans Who Train Them
• Mighty AI’s Training Data as a Service Platform is driven by
data science and a community of known members.
• We train and qualify all community members on our tools and
annotation tasks.
• We even target individual tasks at the right people with the right
skills and domain expertise.
• Our proprietary machine learning algorithm protects against the
risk of subconscious bias in data science.

11.
Myth 4:
“My use case is too
difficult.”

12.
12
Reality: The Experts Excel at Complicated Use Cases
• Mighty AI works with companies across industries, and our
projects range from simple image classifications to full
segmentations of complex road scenes.
• With one data scientist, annotations take too long, are too
complicated and lead to a decline in quality over time—but we
send broken-up microtasks to a large set of qualified community
members.
• We break up all projects into short, game-like tasks for people to
do in their spare time.
• Our own data science monitors results and quality, so your team
doesn’t have to.

13.
- Brian Kim, VP of Product Management at GumGum
“We need very highly specialized annotated datasets. The Mighty
AI platform makes it easy.”

14.
Learn more
at
mty.ai