What is Data Annotation in Machine Learning?
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, and many other AI and robotics applications.
Data Annotation Market Size
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.
At present, the commercialization of Artificial intelligence has reached a stage of basic maturity in terms of computing power and algorithm. In order to better meet the landing needs and solve specific pain points in the industry, scaleable annotated data for algorithm training is still indispensable.
It is said that data determines the success of AI implementation. Moreover, the forward-looking data products and highly customized data services have become the mainstream of industry development.
In the next few years, the data annotation industry will have the following trends and challenges.
Trend: Industry Reshuffles, Intensifying competition
After years of development, the data annotation industry has entered a period of rapid growth.
From the micro point of view, the continuous expansion of the market means more participants and more competition. Due to the low entry threshold and the excessive dependence on human resources, a large number of small and medium-sized data service providers are clustered in the industry.
With the improvement of the technical threshold, the demands Changement of AI enterprises, and the increase of labor costs, small and medium-sized data service providers will face the increasing cost pressure. In the next 1–2 years, the industry will likely usher in a wave of “shuffling period”.
With the speeding up of commercial landing, the AI companies also put forward new requirements for data service suppliers. The quality, refinement, customization is more and more popular on the demand side. On the supply side, technical strength, controlled management, and so on have brought a new challenge.
Challenges: the Outmoded Industry Development Under the New Demand
As mentioned, “more forward-looking data products and highly customized data services have become the mainstream of industry development”. However, the current level of industry development is far from meeting these new needs. The data annotation industry faces the following challenges:
1. Different industries and business scenarios have different requirements for data annotation. The existing annotation ability is not refined enough to support customization services.
Data annotation has a wide range of application scenarios, including autonomous driving, intelligent security, new retail, AI education, industrial robots, intelligent agriculture, and other fields.
Different scenarios have different labeling requirements, such as automatic driving industry mainly focuses on pedestrian recognition, vehicle identification, traffic lights, road recognition, etc. The security industry mainly focuses on face recognition, face detection, visual search, key points, and license plate recognition.
2. Customer points: low labeling efficiency, poor data quality, lack of human-machine cooperation.
The particularity of the data annotation industry determines its high dependence on manpower. Currently, the mainstream annotation method is that the annotator completes the work such as classification, picture frame, annotation, and tag with the help of labeling tools.
Due to the uneven ability of the annotators and the imperfect functions of the annotation tools, the data service providers are deficient in annotation efficiency and data quality.
In addition, at present, many data service providers ignore or do not have human-machine cooperation capability, and do not realize the mutual effect of the AI industry on data annotation.
For instance, the AI-assisted tool can not only effectively improve efficiency but also greatly improve accuracy.
3. Data labeling service providers, who rely on crowdsourcing and subcontracting, fail to guarantee quality.
At present, data labeling mainly relies on human resources, and human resources account for the most part of the total cost. Therefore, many data service providers give up their in-house labeling teams and turn to subcontract to complete the labeling business.
Compared to the in-house labeling team, crowdsourcing and subcontracting have lower costs and become more flexible. However, the labeling loop is too long to cooperate and data quality is difficult to control. From a long-term perspective, the in-house labeling team is more in line with the needs of industrial development.
4. Data annotation tasks based on crowdsourcing and subcontracting mode will cause data security issues and encounter the risk of privacy leakage.
The demander side of some special industries, such as financial institutions and government departments, pays particular attention to data security. However, some data labeling enterprises distribute and subcontract these sensitive data to other service providers or individuals only for cost consideration, which brings huge potential data leakage risks. How to establish a perfect data security protection mechanism has become a vital factor to consider.
To sum up, the data annotation industry has a broad prospect, but it also faces many challenges.
In the foreseeable period of industry transformation, both medium-sized and large-sized data service providers cannot avoid the changement. Only by enhancing the self-developed technical strength and by speeding up the evolution can they be competitive in the new era.
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