The advent of the model era is accelerating the transformation of intelligent development from model-centric to data-centric. From training, iteration to application, the demand for high-quality data services runs through the model life cycle. As the starting point for AI to understand the world, the data annotation industry is also ushering in a critical reshuffle.
Recently, the qubit think tank released the "China AIGC Data Labeling Industry Panorama Report", which depicts the panorama of China's data labeling industry from multiple perspectives such as the current situation, changes, industry development and market scale of China's data labeling industry. Based on the construction of data infrastructure, the understanding of large model AI technology, and the deep cultivation of the industry, and other factorsThe qubit think tank selected the top 20 representative institutions of the data annotation industry that are most noteworthy in China, and Beisai Technology was selected as a leading AI data infrastructure service provider in China
The key takeaways from the report are as follows:
The demand for quality data services runs through the life cycle of the model, and the relationship between the upstream and downstream of the industrial chain is closer and more coupled
Large model paradigms have poured into data labeling, and the efficiency of automatic labeling has been further improved
Data annotation has shifted from labor-intensive to knowledge-intensive
The domestic market size of 10 billion yuan, synthetic data has the highest growth rate;
The multi-field marking of academic qualifications has become a rigid need, and the shortage may reach one million.
Data annotation in the era of large models.
The model is a data-centric product, and around the whole life cycle of model development (including pre-training, supervision and fine-tuning, RLHF, red team testing, benchmarking, etc.), professional data service providers, model companies, AI companies, etc. have all come up with relevant data solutions, and some of them are based on station-based and customized services.
As the model continues to be updated and iterated in real time and landed in multiple vertical fields, especially through intelligent and intelligent explorations, how to quickly expand to more real edge scenarios, quality scene data will also become a rigid demandThe quantity and quality of data are highly determinedSets the upper limit of the model's energy
As the industry's leading large-scale model data service provider, from open source to closed source, from cleaning to distillation, from RLHF alignment to data task annotationOne-stop data solution。It helps large models achieve efficient training, fine-tuning, and customization, and empowers the intelligent upgrade of thousands of industries.