As the artificial intelligence movement accelerates in limitless directions, one thing is certain: High-quality data is the linchpin. To get data that’s of the highest caliber, you need humans to interpret it with near-perfect accuracy—especially in computer vision environments like autonomous driving. Even the smartest AI companies are struggling to do this at scale. But why? Learn more on our blog.

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