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What is Data Annotation and What is the Key Advantage in 2021?

Data Annotation and Market Size

Data annotation technique is used to make the objects recognizable and understandable for machine learning models. It is critical for the development of machine learning (ML) industries such as face recognition, autonomous driving, aerial drones, robotics, and many other AI and applications.

The global data annotation market was valued at US$ 695.5 million in 2019 and is projected to reach US$ 6.45 billion by 2027, according to Research And Markets’ report. Expected to grow at a CAGR of 32.54% from 2020 to 2027, the booming data annotation market is witnessing tremendous growth in the forthcoming future. The data annotation industry is driven by the increasing growth of the AI industry.

data annotation market research
source: statista

Data Annotation Process is Tough

Unlabeled raw data is around us everywhere, such as emails, documents, photos, presentation videos, and speech recordings. The majority of machine learning algorithms today need labeled data in order to learn and get trained by themselves. Data labeling is the process in which annotators manually tag various types of data such as text, video, images, audio via computers or smartphones. Once finished, the manually labeled dataset is fed into a machine-learning algorithm to train an AI model.

However, data annotation itself is a laborious and time-consuming process. There are two choices to do data labeling projects. One way is to do it in-house, which means the company builds or buys labeling tools and hires an in-house labeling team. The other way is to outsource the work to renowned data labeling companies like Appen, Lionbridge.

The booming data annotation market has also stimulated multiple novel players to secure a niche position in the competition. For example, Playment, a data labeling platform for AI, has teamed up with Ouster, a leading LiDAR sensors provider, known for the annotation and calibration of 3D imagery in 2018.

Customer Pain Points

Here are some extractions from the Reddit discussion groups:

1 Lack of QA/QC process.

2 Lack of monitoring, some labelers are good while others are bad at the job. Would be great to separate performances based on labelers.

3 Software was not designed for labelers or encourages mistakes.

The list goes on…

Flexibility is the Key Advantage in 2D Images Data Labeling Loop

As the high-quality standard, data security, and scalability are the most important measurements in labeling service, we may have a look at the rest competitive parts, for example, flexibility and customer service.

In machine learning, in each round of testing, engineers would discover new possibilities to perfect the model performance, therefore, the workflow changes constantly. There are uncertainty and variability in data labeling. The clients need workers to respond quickly and make changes in workflow, based on the model testing and validation phase.

Therefore, more engagement and control of the labeling loop for clients would be a key competitive advantage as it provides flexible solutions.

End

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Why not have a try?

Relevant Articles:

1 Data Labeling Service: How to Ensure Data Quality for Machine Learning and AI Projects?

2 Data Annotation: By Typing Captcha, You are Actually Helping AI Model Training

3 Data Annotation Service — From the Backstage to the Front Stage?

4 Data Annotation and Labeling for ML Projects in 2021

5 How to Make Data Annotation More Efficient?

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