Data labeling is the process of adding tags to raw data to help machine learning models learn. It is time-intensive and prone to errors. There are several approaches to data labeling including crowdsourcing, in-house labeling, outsourcing, and AI-assisted labeling. Outsourcing to professional data labeling companies offers an efficient solution that can optimize costs without sacrificing quality and help businesses gain profits through machine learning.

1. Add Value to your
Business With
Professionals
Ai Data labeling Services

2. Table Of Content
2
i. Introduction
ii. Understanding the Process
iii. Knowing the Challenges
• Time Intensive
• Prone to Errors
• Inconsistencies
Iv Taking the Right Approach
• Crowd sourcing
• In House
• Outsourcing
• AI Data Labeling
V Conclusion

3. 3
What comes to your mind when you think of artificial intelligence, machine learning,
and deep learning?
A generalized notion of how we perceive AI is movie like robots that outperform
human intelligence. Some of us also think of AI as machines that consume data and
learn by themselves. Apart from this, we also consider intelligent virtual agents, deep
learning, neural networks, machine learning, predictive analytics, etc., as ‘other
names’ for AI.
Introduction

4. 4
Damco Solutions
We offer customized data annotation
services to a wide network of B2B and B2C
clients. Having in-depth experience, our
annotators develop enhanced training sets
to be fed into machine learning algorithms

5. 5
Understanding the
Process
● Data labeling is the process of adding tags
to raw data to help the machine learning
models easily calculate the attributes. These
tags show the smart model the target
attributes or answers, it is expected to
predict. A tag or a label is the descriptive
element that helps the machine learning
algorithm learn by example.

6. 6
Know the Challenges
Though data labeling is not a rocket to be launched in the sky, yet is a significant undertaking.
Errors or inaccuracies while adding labels deviate from the outcomes.
Mentioned here are some of the significant challenges associated with the process

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Time Intensive
Getting massive volumes of data (specifically for highly
specialized niches like healthcare) is not only a tough
challenge, but also a resource-intensive task. The time-
consuming nature of the data labeling process makes it
extremely difficult for human labelers to manually add tags
to the input data sets. Data preparation takes up around 80%
of the project time within the full cycle of ML development.

8. Prone to Errors
8
The manual tagging and labeling process is prone
to human errors; no matter how experienced or
attentive the annotators are.
It is because the human labelers have to deal with
massive volumes of raw data—a person marking
150,000 pictures with up to 10-15 objects each.
Just imagine!

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Inconsistencies
As different annotators have different levels of
expertise, labeling criteria and descriptions
themselves might be inconsistent, thus adding on to
the challenges list.
Besides, there can be disagreement on labels
between annotators. For example, one reviewer
might take a hotel review as sarcasm and add a
negative label; whereas another expert may score it
positive.

10. Taking the Right Approach
10
There are numerous approaches to the data labeling process. The style you chose varies
according to the amount of data to be labeled, the complexity of the problem statement, and the
size of the data science team.
And how can you miss your financial resources and available time? Let me take you through
the different approaches quickly.

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Crowd sourcing
There are dedicated crowd sourcing platforms like Amazon
Mechanical Turk. These platforms need you to sign in as a
requester and assign the labeling tasks to contractors
available there.
It is a comparatively affordable and relatively faster
approach but doesn’t guarantee the quality of the labeled
data.

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In-house
In-house data labeling is performed by a competent
pool of professionals within the company. Usually, it
is considered as the holy grail; however, it is not the
only solution!
You can opt for this option when you have adequate
resources in terms of time, money, and trained
personnel. Though it offers the highest labeling
accuracy possible, it is slow on the flip side.

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Outsourcing
Outsourcing is just another and perhaps the most
efficient way to get things done within the stipulated
time and budget. You can either consult a professional
or collaborate with accomplished data labeling
companies to outsource such core tasks.
They have the potential required to perform the labeling
process accurately; thereby, ensuring excellence in
every outcome.

14. 14
AI Data Labeling
Yes, you get that right! The data labeling process can
also be assisted by software. Labels and tags can be
added automatically via the active learning technique.
Putting it simply, human annotators create an AI auto-
label model that scripts the raw data. The outputs are
then verified. If the model fails to accurately label the
datasets, human-in-loop labelers make the corrections
and further re-train the model.

15. Conclusion
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As a decision-maker, you must leverage an approach that
helps you in attaining profits across different aspects.
Hence, collaborating with data labeling companies is the
most trustworthy solution to optimize operational costs
without impacting the quality of outcomes.

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Contact Us
2 Research Way, Princeton, New Jersey 08540, USA
+1 609 632 0350
info@damcogroup.com
https://www.damcogroup.com/data-support-for-ai-ml

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Thank You