For the whole artificial intelligence industry, there is a great demand for AI technology in the fields including driving, security, finance, industry, medical care, education, etc. The rapid development of AI technology based on machine learning depends on the richness of the underlying big data, and a powerful model needs a data set with a large number of samples as its foundation. The quality and diversity of data will have a significant impact on the success or failure of algorithm models. The delivery of high-precision AI data not only helps the AI industry to land in scenes, but also brings a better user experience.
At the data level, with the development of AI technology, the data scale is constantly improving. According to IDC’s calculation, the global data scale will reach 163ZB； in 2025; At the same time, the AI data service industry has entered the stage of deep customization, and the service of data customization is carried out according to different scenarios and requirements, and the AI data requirements also transition from general simple scenarios to personalized scenarios.
In order to solve the practical problem of AI industrialization, cloud measurement data summed up many experiences and solutions, and used them in practice to help the development of the whole artificial intelligence scene application. Through its own technology, it has overcome the difficulties, designed scientific and standardized data processing processes from task creation to final acceptance, and flexibly met the diverse and high-precision data needs of customers. It has successively launched products and services such as "data scene laboratory", "AI data set management system" and "cloud measurement data annotation platform", providing high-quality, scene-based and large-scale processing of perceived data for many AI-related enterprises such as intelligent driving, smart city, smart home, smart finance and new retail.
Of course, it is not easy to keep the leading position of technology and industry in the tide of artificial intelligence. From the perspective of attack and exploration, it is not difficult to see that the reason why cloud measurement data can become an industry leader is not only due to the toughness of technology and product strength, but also the homeopathic development of service model and service concept, thus continuously injecting new vitality into the artificial intelligence industry and providing new kinetic energy for development.
First of all, data came into the market when the industry was on the rise, and the cloud measurement data with the first-Mover advantage was not satisfied with the dividends at that time, but constantly increased the technical input and improved the production efficiency by improving the technical level. Give full play to the power of "underlying technology+service capability" and provide end-to-end training data service solutions in autonomous driving, smart home, smart city and smart finance and other industries.
At the same time, cloud measurement data keeps forward-looking forecast on the development trends of hot industries and technologies, and prepares relevant tool chains and data service capabilities in advance to ensure adequate preparation to meet new AI data requirements. In the current AI data industry chain, there is a keen discovery of cloud measurement data, and there is still a lack of a systematic data solution for AI engineering. However, this systematic data solution for AI engineering is needed by many industries. In this context, the cloud measurement data industry launched a new generation of data solutions for AI engineering, which was undoubtedly a timely rain for many industry customers and solved their actual needs.
For this reason, cloud measurement data has launched a new generation of data solution for AI engineering. Through the mature data management and labeling platform, this solution can complete system integration with enterprises, support enterprise-defined pre-labeling, algorithm interface, personnel management, project management system and secure delivery of software and hardware support. Under the labeling environment that ensures data privacy and security, it highly supports the efficient circulation of data required by enterprises, continuously performs data processing tasks, and improves the large-scale production efficiency.
For example, in the field of automatic driving, it can realize Data cleaning and labeling in the data closed loop of DataOps (that is, the combination of data and Operations) of automobile enterprises, and improve the circulation efficiency by 2 times compared with the original process; In the aspect of retail goods inspection, through the cloud measurement data labeling platform, the container inspection data continues to flow back, and visual review and modification are carried out based on the pre-labeling results of the algorithm, which improves the efficiency by 3 times compared with manual labeling.
"Walk alone fast, go far". In the era of industrial intelligence, we can’t just rely on one enterprise to fight alone. The double value of industry and society will produce compound interest effect. Cloud measurement data also knows this well. It is also actively promoting the standardization of artificial intelligence data industry, and has participated in the compilation and release of "Requirements and Methods for Marking Point Cloud Data of Intelligent Networked Car Lidar" and "Requirements and Methods for Marking Image of Intelligent Networked Car Scene Data", contributing experience and wisdom to industrial intelligence, and promoting the construction of standardization system in the vertical field of AI data service. In addition, it also participated in the first series of standards of "Model/MLOps Capability Maturity Model", which filled the gap of the development and management standards of machine learning projects at home and abroad.
As the vanguard of artificial intelligence data services, cloud data is actively promoting the accelerated development of AI training data services, contributing experience and wisdom to industrial intelligence, thus becoming a new paradigm of industry development. I believe that next, cloud measurement data will continue to improve. While continuously enriching its own service capacity building and deep cultivation technology, it will maximize the value of training data and deliver more excellent data support for artificial intelligence scenes.