Shulou(Shulou.com)06/03 Report--

With the acceleration of AI commercialization process, more forward-looking mass data set products and highly customized services have become the main service forms of AI basic data service industry. This places new demands on the data delivery capabilities of data service providers.

At present, there are three main business modes in the data labeling industry: supplier subcontracting mode, crowdsourcing mode and self-built team mode.

I. subcontracting mode

The subcontracting model works in such a way that a data provider receives a project and distributes it to a partner vendor for execution.

The main advantages of this mode of operation are:

1. Low operational risk. By distributing the project to suppliers, problems such as data disconnection can be effectively avoided;

2. Cost controllable, cash pressure is small. The settlement method of subcontracting is calculated according to the actual data delivery volume, the expenditure cost proportion is small, and there is no need to bear the salary of the labeled team, and the cash pressure is very small.

However, there are many problems with subcontracting:

1. Data quality issues. In subcontracting mode, the quality of data annotation is mainly decided by suppliers. If suppliers have problems in management training, it will be difficult to ensure the quality of data sets.

2. The duration cannot be guaranteed. The data labeling industry is characterized by high turnover of employees, and suppliers are prone to shortage of personnel, resulting in project delay.

II. crowdsourcing model

Crowdsourcing is a model that integrates individuals and suppliers into a platform to complete a project.

The main advantages of this mode of operation are:

1. Flexible adaptation to project needs, for specific project allocation of appropriate professionals;

2. Low cost, direct docking of labelers, no middlemen.

The main problems are:

1. Quality is difficult to control. Under the crowdsourcing mode, the overall quality of the tagging staff is difficult to control, and various quality problems are easy to occur.

2. Cash flow pressure is high. The operation of the platform and the retention of personnel need to invest a lot of expenses;

3. It is difficult to guarantee the construction period. Individual participation in the project is difficult to unify and effectively manage, working hours are difficult to unify, and project delays are serious.

III. Self-built labeling team

Self-built annotation team is a mode in which data service providers establish dedicated data annotation teams, manage them uniformly, and complete all annotation tasks by self-built teams.

Manfu Technology Labeling Team

The main advantages of this mode of operation are:

1. High data quality. The self-built labeling team has unique advantages in internal training management, and there is no middleman, the demand is more clear, and the data quality is guaranteed;

2. High labeling efficiency. The self-built team has stable internal personnel and clear organizational structure, which can efficiently complete the established tasks.

However, the self-built team also has certain risks, mainly reflected in the cost control problems under the condition of project interruption.

IV. Self-built labeling team is the future of data service providers

At present, the common project operation modes in the data labeling industry are mainly subcontracting mode and crowdsourcing mode, and most data service providers do not list the self-built labeling team in the development plan.

However, subcontracting mode and crowdsourcing mode have great problems in data quality and labeling efficiency, especially in the context of large-scale commercial application of AI, lower quality data sets and lower labeling efficiency have seriously slowed down the development of AI industry and become obstacles to the development of the industry.

Compared with these two methods, self-built labeling teams can obviously better meet the actual needs of large-scale commercial applications of AI. Through unified training and management, and the establishment of an effective multi-level quality management system internally, it can not only effectively improve the quality of labeled data sets, but also greatly improve its own data service delivery capability.

With the quelling of the last round of AI entrepreneurship boom, the demand-side market has transitioned from rough to refined, and problems such as increased project requirements, profit compression and rising management costs have forced a number of small and medium-sized data service providers to leave early. In the next one to two years, the industry will reshuffle again.

This has brought great tests to the productivity, refined management ability and profit control ability of brand data service providers. By improving their advantages in quality and efficiency through self-built labeling teams and establishing deep barriers in the industry in advance, it is the next strategic highland that a large number of brand data service providers need to pay close attention to and seize.

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