Choosing The Right Data Annotation Option: Pros And Cons

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Choosing The Right Data Annotation Option Pros
And Cons
Rolling out machine learning models requires
high-quality data. Sometimes, businesses realize
this when a model is not performing well, and
that's already too late. Other times, a company
may realize that the raw datasets it has been
working with are not sustainable for advancing
its computer vision, natural language
processing, or recognition initiatives. While
unstructured (unlabeled) data is plentiful,
businesses need quality labeled datasets in
which to train and evaluate their models. As the
number of AI applications and use cases has
exploded, the need for quality labeled data has
grown exponentially. Favorably, data annotation
serves as an answer to these challenges. To help
you better, we've evaluated the pros and cons of
various data annotation options available.
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• What Is Data Annotation?
• The process of attributing, tagging, or labeling
  data to advance contextual understanding is
  known as data annotation. These processes are put
  in place to create relevant metadata for
  machines so that they can perform various tasks,
  such as classification and regression.
• Labeled datasets in supervised learning serve to
  train ML algorithms. Without such a process,
  automatic analysis, understanding, and
  decision-making are impossible. For instance,
  while sifting through unlabeled data, every
  image will be the same for machines because they
  would not be able to process contextual
  differences inherently.
• Different Methods For Data Annotation
• While annotating their raw data, businesses can
  choose one of the following options
• Open-source tools with an internal team of
  annotators
• Paid platforms with an internal team of
  annotators
• Paying a vendor to annotate data with a specified
  platform
• Paying a vendor to annotate using their own
  platform
• Choosing the right option among these can be
  daunting. Therefore, we've evaluated the pros
  and cons of the various data annotation options.
  But before that, keep these in mind while
  choosing an annotation tool

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• While choosing an annotation tool, businesses
  must consider the following features
• Annotation Method
• Dataset management
• Workforce management
• Data quality control
• Security
• Open-Source Tools With Internal Annotators
• The simplest and cheapest data annotation option
  is open-source tools internal annotators.
  Providing internal annotators with open-source
  tools is highly recommended for small projects
  where companies want to plan and strategize an
  idea for AI/ML project model. However, it is not
  suitable for large-scale business operations.
• Pros
• The open-source data annotation tools come with a
  quality assurance mechanism ensuring the
  datasets are up to the mark.
• Open-source data annotation makes handling a
  large amount of information less time-consuming.
• Cons

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• Although these tools are free, companies might
  still require team members who have experience
  in using the tools.
• The method is not suitable for those planning to
  scale their project.
• Paid Platforms With Internal Annotators
• There are many paid data annotation platforms
  available online. Using them is viable for
  companies that have well-established processes
  and want to put their own annotation staff to
  work. However, as the sophistication level and
  data volume grow, teams might need specialists to
  complement the endeavors of the internal team,
  especially when the latter isn't technically
  adept.
• Pros
• Paid platforms constitute project management
  features that help to ease up the data
  annotation process.
• They further help avoid obstacles one might
  otherwise face while modifying open-source
  software or creating their own annotation
  platforms.
• This method ensures high-end data security and
  sophisticated compliance needs.
• Further, it utilizes a dedicated workforce to get
  the job done.
• Cons
• Lacks customization options that are available in
  purpose-built annotation platforms.

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• Businesses, at some point, might need expert
  technical professionals who are competent at
  using paid platforms and making the most out of
  them.
• Paid platforms may not be always suitable for
  complex projects with specific requirements.
• Paying a Vendor to Annotate Data with Specific
  Tools
• Data annotation services provided by vendors are
  suitable for enterprises with specific needs for
  quality assurance and compliance requirements.
  This method lets them scale their project,
  perform all the data annotation tasks with the
  tool of their choice, and reduce internal
  employees' workload. As such, this method bodes
  well for accommodating large-scale projects.
• Pros
• Reduces employees' workload so they can focus on
  other parts of development.
• Eases project scalability and helps save time in
  the long run
• Choosing the right vendor can provide the highest
  possible level of data quality and assurance
• Cons
• It might take some time for the vendor to
  understand the proper workflow
• Businesses are responsible for investing time and
  effort in selecting the right software and
  functionality.

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• Paying a Vendor to Annotate Using Their Own
  Platform
• Vendors customarily use specific data annotation
  tools or build tools with a workflow of their
  choice. As such, they can easily make changes
  based on the business needs and requirements.
  This option also helps them to be more flexible
  and operate effectively and efficiently.
• It is also THE most comprehensive method as the
  vendor handles all the aspects of the annotation
  process. In this method, the client can specify
  the project needs, and the vendor will determine
  the strategy keeping in mind the accuracy,
  speed, and cost.
• Pros
• The learning curve is less when compared to using
  specific tools.
• Reduces the need for intervention on the client's
  part.
• The best for companies looking for a professional
  to handle end-to-end data annotation.
• Cons
• It can get costly owing to customizations and
  related quality assurance initiatives.
• Sometimes, the vendor's software might not be the
  best for the job.

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So, Which Data Annotation Option Is the Best? It
all comes down to what the business needs. While
open-source tools and internal annotators are
good options to start with, these do not provide
the same level of flexibility and customization
as paid software. And even with paid platforms
in their arsenal, businesses might not achieve
high-end quality and control over data through a
dedicated staff. Eventually, they might turn to
an external team or completely outsource the
project. Regardless of the project's cost,
businesses must think through their needs to
choose the right annotation option. What data
annotation option are you going with? What other
options do you think are viable? Share your
thoughts with us. Click here to know more about
Data Annotation Self-Driving Cars Powered
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