Shaip

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

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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

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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.

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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

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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

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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

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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

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