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AI for Good Innovate for Impact
4 Sequence Diagram 4.3 - 5G
Figure 2 Sequence Diagram of AI Middle Platform Pipeline consisting of the following
components:
• Data phase: build high-quality datasets, structured data, unstructured data, knowledge
graph.
• Computing power: integrate smart arithmetic, generalized arithmetic, storage, etc. for
training and inference.
• Standard training and inference stage: integrate pytorch, LangChian, TensorRT and other
frameworks to provide a low-code code development environment for model training
and inference.
• Model service stage: the model produced by the model, as a service provided to the
outside world, including large language models, multimodal models, speech large
models, small models, and other components of the MaaS service.
• Model application construction stage: Relying on the big model Agent (intelligent body),
video toolchain, voice toolchain, and other application toolchains, the model's reasoning
ability is used to plan and disassemble the task, and external plug-in tools are used to
complete complex tasks[5].
• Industry application stage: Industry AI big model applications, such as RAG class
applications, ChatBI class applications, and Agent class applications, including
transportation big model application assistants, tourism guide application assistants,
and grassroots governance assistants.
5 References
[1] “Nanjing University of Posts and Telecommunications,” [Online]. Available: http:// www
.njupt .edu .cn/ .
[2] Che Shaoshuai, Zhang Wei, Design of Visual Perception AI Platform Based on Cloud Edge
Collaboration [J]. Nanjing: Jiangsu Journal of Communications, April 2024.
[3] Che Shaoshuai, Research on Multi object Detection and Tracking Technology Based on
O2O Learning [D]. Nanjing: Nanjing University of Posts and Telecommunications Library,
May 2016.
[4] Ministry of Industry and Information Technology (MIIT), Report on the Development
of Artificial Intelligence (2024), China Academy of Information and Communications
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