Accelerate Your Data Annotation Efforts

Today, data drives an organization. Massive amounts of data come from every department imaginable, be it finance, marketing, operations, customer service or human resources.

The problem is not of collecting data but using it effectively and efficiently. Luckily, digital age comes with cutting-edge technologies equipped for the task, like Machine Learning, Artificial Intelligence, visualization and data insights. Together, such tools comprise the data annotation efforts.

Grand View Research published a report recently claiming that the data annotation industry will grow to $1.6 billion by the year 2025.

Data Annotation Importance in ML (Machine Learning)

ML or Machine Learning is an offset of Artificial Intelligence. This technology offers a wide variety of applications. As a result, it is used extensively in all industries like marketing, healthcare, finance, consumer behavior, gaming, utilities and more.

Data scientists code different algorithms based on ML to derive useful insights from the available data. Depending on the need of the business, scientists use different codes on the same set of data to draw comprehensive insights.

Data Annotation Importance in AI (Artificial Intelligence)

Data annotation is the process of labelling the available data. Through the annotation process users can discover the formats of different data, like audio, text, images or video. ML requires properly labelled data to comprehend input patterns. In short, how well you annotate your data decides the success of your ML model.

But data annotation can be time-consuming. In fact, in a typical ML project around 40 to 50 per cent of the time goes into data annotation. Deploying the actual ML model comes much later after the labelling of data is over.

Data scientists do not enjoy the labelling process. It takes a lot of time and requires laborious work.

Accelerate Data Annotation

By using active learning technology, companies can minimize the time and effort spent on data annotation. A good example of such technology is Zastra.

Here are the features of an efficient data annotation software:

  • Allow collaborations in real-time and reduce time-to-market.
  • Use active learning technology for classification, detection, segmentation and localization of objects.
  • Provide topological data analysis.
  • Prevent duplication of data.
  • Allow easy export of labelled data sets and models.

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