With the rise and development industries  of artificial intelligence, data annotation has also become an emerging industry. In daily work and life, data labeling has been used in many scenarios. So, which industries need data labeling? Let’s introduce it below.

 

What is Data Labeling?

Data labeling refers to the process of identifying unstructured data for training machine learning models. Today, we are surrounded by vast amounts of raw data, which can be in a variety of formats, including images, video, and text. Machine learning models use appropriately labeled data to recognize objects, understand emotions and perform functions such as driving or speaking.

Which industries need data labeling?

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High-quality data annotation can generate real data for machine learning. Deep learning applications can be used for data labeling in many industries, including autonomous driving, medical AI (employment), finance, business, and more.

1. Autonomous driving

Data annotation services are a way to help self-driving technology by labeling images of a car’s environment.

2. Medical artificial intelligence

Annotating medical data includes labeling medical imaging data in order to detect abnormalities and diagnose diseases. This is done to improve the health of patients.

3. Business

Data annotation in commerce allows experts to classify e-commerce content using various attributes to enhance customer experience and efficiency.

4. Geospatial Technology

Public sector data annotation provides solutions for sensitive data processing at satellite, drone and aerial levels. This is efficient, effective and user-friendly.

5. Finance and insurance

Data annotators organize relevant information from large amounts of unstructured visual data to automate manual processes and procedures.

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Data annotation is a key factor in the effectiveness of any AI model, because the only way for image detection AI to recognize faces in photos is to label a large number of photos with the word “face” in them. A machine learning model does not exist without labeled data. The main purpose of data labeling is to label data, and labeling data is the first step in every data flow. Data annotation is critical to artificial intelligence and machine learning, both of which bring enormous value to humanity. Today, data labeling is no longer just a job that can be done purely by humans, and technology orientation will put forward higher requirements for industry talents.

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