Data Labeling is the process of identifying raw data and adding one or more meaningful and informative labels to provide context.

1. What is Data Labeling? Everything a
Beginner Needs to Know

2. What is data labeling
In machine learning, data labeling is the process of
identifying raw data (images, text files, videos, etc.)
and adding one or more meaningful and
informative labels to provide context so that a
machine learning model can learn from it. For
example, labels might indicate whether a photo
contains a bird or car, which words were uttered in
an audio recording, or if an x-ray contains a tumor.
Data labeling is required for a variety of use cases
including computer vision, natural language
processing, and speech recognition.
Source: https://www.shaip.com/blog/what-is-data-labeing-everything-a-beginner-needs-to-know/

3. Global Data Labeling Market
AI models need to be trained extensively for being
able to identify patterns, objects, and eventually
make reliable decisions. This is where data labeling
helps in labeling information or metadata, to focus
on amplifying the understanding of the machines.
As per the latest report the data labeling market is
presumed to reach a massive valuation of $4.4
billion by 2023. View the full infographics to learn
more:
Source: https://www.shaip.com/blog/what-is-data-labeing-everything-a-beginner-needs-to-know/

4. 7 Data Labeling Challenges
AI feeds on copious amounts of data to continually
learn and evolve. Tagging objects within textual,
image, scans, etc. enable algorithms to interpret the
labeled data and get trained to solve real business
cases. The task of labeling data must meet 2
essential parameters: quality & accuracy, however, it
comes with several challenges. View the full
infographics to learn 7 Data labeling challenges
companies face.
Source: https://www.shaip.com/blog/what-is-data-labeing-everything-a-beginner-needs-to-know/

5. Types of Data Labeling
There are various types of data labeling modalities,
depending on what type of data you deal in.
Although you can segregate data labeling
conceptually, the majority of problems in which AI
models are being built to address them can fit into
one (or many) of the below annotation tasks these
include, text classification, audio transcription,
image, and video labeling, semantic labeling, and
content categorization, etc. View the full
infographics to learn more:
Source: https://www.shaip.com/blog/what-is-data-labeing-everything-a-beginner-needs-to-know/

6. 4 Key Steps in Data Labeling
Data annotation is a detailed process and involves
the following steps to categorically train AI models:
• Data Collection
• Data Labeling & Annotation
• Quality Assurance
• Deployment / Production
Source: https://www.shaip.com/blog/what-is-data-labeing-everything-a-beginner-needs-to-know/

7. Factors to consider while choosing the right tool
Selecting the right labeling tool to accurately train
your AI models is of utmost importance. The right
set of data labeling tools is synonymous with a
credible data labeling platform that needs to be
selected, keeping in mind a lot of factors. View the
full infographics to know different factors that one
should consider:
Source: https://www.shaip.com/blog/what-is-data-labeing-everything-a-beginner-needs-to-know/

8. Build vs Buy
Still confused as to which is a better strategy to get
data labeling on track, i.e., Building a self-managed
setup or Buying one from a third-party service
provider. Here are the pros and cons of each to help
you decide better:
Source: https://www.shaip.com/blog/what-is-data-labeing-everything-a-beginner-needs-to-know/

9. Read the Data Annotation / Labeling Buyers
Guide, or download a PDF Version.
CLICK HERE TO DOWNLOAD