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Should Enterprises Use No Code Tools? What problems should we pay attention to? Cai Yuanhong, Chief Data Scientist of the Artificial Intelligence Technology Foundation, believes that each No Code platform or tool has different strengths, and it will have a certain effect on small and medium-sized enterprises that cannot cultivate data teams. However, it doesn’t matter if you don’t know how to write programs. You must have a basic knowledge of statistics and a certain understanding of data science. “It just doesn’t let you write programs, which doesn’t mean you can ignore them. Meaning also requires basic knowledge.” How do you generate reports that are useful for forecasting or decision-making if you don't have the knowledge? It is also impossible to ask the system to generate a model out of thin air. As for what background knowledge do you need? Cai Yuanhong said, it depends on which type of No Code tool you choose? For example, some types of comparative statistics require knowledge of statistics; some platforms are strong in modeling or forecasting, so they need to understand machine learning or statistical inference. Of course, it is best to have basic knowledge in the fields of statistics, data science and machine learning. Cai Yuanhong added that many university departments currently study statistics, and this knowledge can help users gain some understanding of the data when using the No Code tool. The understanding of machine learning is to help users understand the strengths of these model tools and the prediction results generated, instead of blindly believing. Will engineers lose their jobs in the No Code era? "I think the human brain can do more creative things," Cai Yuanhong believes that if the machine can solve trivial things, it can save the energy and time of engineers. There are many roles in the execution of an AI project, but No Code tools cannot replace all of them. For example, the data entered into the computer still needs to be processed so that it cannot be too dirty, and the characteristics of each company's data are different, so the method will be different. For example, although the method of complementing missing values is a common practice, sometimes it still needs to be processed differently according to different data characteristics, which may require the knowledge of engineers or data scientists to clean up or organize. For companies that cannot support a large team, No Code's platform or tools have already helped complete part of the tasks, so there is no need to employ many people to do data statistics or forecasts. For engineers, it is also possible to focus on places that require more human thinking. For example, there are many methods of data mining, and it is impossible for machines to be good at all of them. What engineers need to do is to do things that require more ingenuity, or tasks that are oriented towards overall consideration. Will large enterprises need No Code tools or platforms? Cai Yuanhong said that sometimes companies have different departments, and it is impossible to have a data scientist for each department, or the data engineering personnel may be concentrated in a certain department, and each engineer has different tasks and priorities. Sometimes No Code tools or platforms can help, as long as there is a person who can use statistics.
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