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Whose future is data tagging?

2025-01-19 Update From: SLTechnology News&Howtos shulou NAV: SLTechnology News&Howtos > Internet Technology >

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Shulou(Shulou.com)06/02 Report--

"With the continuous growth of artificial intelligence industry, the artificial intelligence ecological chain is also taking shape. As an important link in the ecological chain, data labeling is being paid attention to by more and more people. "

Artificial intelligence is still in its early stages of development. Although artificial intelligence enterprises such as Spitz and Horizon Robot also took the lead in proposing the concept of "closed-loop learning". But there is no doubt that supervised learning will remain the dominant concept of machine learning for a long time.

When it comes to supervised learning, there is no way to bypass data labeling, because a large number of algorithms and models need to verify whether the labeled data is feasible, and then realize the positive improvement of model recognition rate by continuously improving the quality and quantity of the data itself. OK, then let's talk about whose future data labeling is in combination with the theme of the article. At present, more and more enterprises specializing in data labeling have emerged in China, covering all kinds of sizes from listed companies to small private workshops with 3-5 people. Although it is said that at present, flowers bloom together and fragrance is scattered everywhere, like all industries, in the end, the industry will slowly settle down and the Matthew effect will gradually become prominent. Because of the particularity of data labeling requirements, super-large data labeling companies have no way to undertake the labeling requirements of general entrepreneurial AI companies because of their high operating costs, but these entrepreneurial AI companies themselves occupy half of the entire artificial intelligence market. So friends who can't become hyperscale data annotation companies actually have their own clear way out. If you are lucky enough to be a super-large data annotation company, then the company itself has explained everything, and this article will not do too much explaining. If the existing resources cannot achieve the company's barbaric growth results within 1-3 years. Then, the following two points can help all friends engaged in data labeling to obtain a clearer development direction.

Service specialization Service specialization refers to deep cultivation in a certain field of data annotation.

Data annotation can be basically divided into three categories according to requirements: image, voice and text. One category can be selected from these three categories as the core service direction.

After selecting the domain, learn the underlying logic of its labeling, such as image segmentation, you need to know what the logic of image segmentation is and why it is segmented.

After selecting the field, only delve into the data annotation requirements of the field. Through continuous acquisition of the requirements of the field, the training cost can be greatly reduced, and the feedback to the requesting company will be more professional.

Systematic operation refers to the use of scientific methods for personnel, data and performance through systems to shorten the time spent on each process and minimize the company's operating costs.

The traditional operation mode cannot solve the core pain points such as data distribution and aggregation, real-time statistics of personnel performance, and real-time feedback of problem data. These problems in the whole data annotation company operation time is often almost the same as pure data annotation, and even more management costs have far exceeded the data annotation itself. This is also why more and more data are labeled as public ××× according to labels, because they will eventually find out through continuous operation of the company: the real ×× saved money, far less than the cost of training and error correction.

LabelHub data annotation management platform, as the only free data annotation management platform at present, can solve the above problems well. Of course, it is not easy to change habits, but it cannot be broken. Just like many state-owned enterprises embrace AI technology, if they lose the initiative in this important link of cost reduction, the strong competitiveness will gradually lose its inherent advantages with the passage of time and high cost.

Large and complete, small and fine will become the basic pattern of the future data labeling industry. Before the pattern is fully formed, it is necessary to set a clear future for the company. At the same time, after the goal is determined, confirm another problem: whether the company's resources can gradually approach the goal in the foreseeable time of the company, if not, we must change our thinking and change it as soon as possible.

For more details, please follow Awakening Vector's official website at www.a×× r.com.

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