Yuhang Jia, the general manager of Cloud Data, believes that in order to overcome the difficulties of the data service industry in the past, it is an inevitable trend to concretize, refine, and quantify data services, and provide service models such as custom data collection and high-precision data annotation to solve these problems one by one.

How do you achieve customization of data collection? How do you do it?

For companies that need to use AI data, it is a known fact that success is also data and failure is also data. This is a known fact. The data here is not just about volume, but also about accuracy. It is important for AI data users to be able to achieve fine data collection and multi-dimensional data annotation.

With the rise in interest in interactive AI, deep AI research and development has become an important part of an enterprise's growth strategy. In general, improving the accuracy of algorithms is an important way for AI to evolve, and therefore places higher demands on data accuracy.

To improve the accuracy of data, cloud measurement data can be customized according to customer needs, providing customers with high precision data in multiple scenes and forms to meet the different data needs of different enterprises. In the early stages of the development of fatigue driving monitoring system, it is difficult for drivers to collect data on dangerous movements. In order to solve this problem, cloud measurement data is used to simulate the driver's behavior related to fatigue driving in the cockpit, such as dozing and playing with cell phones, by establishing a corresponding scene laboratory. The data is then used to train artificial intelligence and form an alert system to ive 工程.

But the most important part of the data service process is the tagging of the data after it is obtained with high accuracy. Without accurate data tagging, the collected data is dead, cannot be activated, and its value cannot be realized. At this point, we have to mention the profession of data tagging.

Back in the day, data tagging was often labeled as labor-intensive and unskilled, but that's not the case with Yuhang Jia. He believes that data tagging has now become a skill-intensive industry. With the rapid development of artificial intelligence, the data tagging industry is rapidly transforming. Teachers of artificial intelligence is their new name. They take data tagging as their mission and strive to achieve true intelligence in artificial intelligence. We are committed to improving the accuracy and quality of data tagging. By continually acquiring a wealth of industry knowledge, technical expertise, and specialized tools, our complex data tagging efforts help make AI smarter.

For example, when tagging vehicle information, traditional data tagging can only ensure that AI accurately identifies 95% of the information such as vehicle type and body color; or some companies need to study road settings, so they only need data about the infrastructure on the road, while others do a study of the automotive industry, so they need data about all the vehicles that pass by on the road. Data accuracy is often determined by the easily overlooked 5%, which requires professional data markers to complete. This is exactly what cloud measurement data is concerned about.

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