In data analysis scenarios, how do companies select and implement large models?

In the data-driven digital era, effective data analysis has become a key factor in business success. As large models bring about breakthroughs in capabilities, the integration of AI and data analysis allows analysis results to better support business and promote the release of the value of internal data within the enterprise. This has become a topic of particular concern to current enterprise users.
How to select large models according to actual business needs? How to ensure the accuracy of data analysis results? Are there any practical cases that we can learn from?
Based on the above background and issues, AiAnalytics will hold a webinar on the theme of "LLM+Data, Promoting the Popularization of Enterprise User Data Analysis" at 19:00 on November 16, to analyze the development trends and trends of the industry. While the company is progressing in implementation, Zhang Yifan, vice president of R&D at Kyligence, was specially invited to bring AI+ data analysis implementation plans and implementation cases within the company, and gave suggestions on large model selection in data analysis scenarios to help companies achieve business upgrades.
Scan the QR code in the long image below to register for the conference.