For sensitive industries such as finance and healthcare, AiPy provides a complete localized deployment solution. Users can specify local LLM service endpoints (e.g. Ollama) through the configuration .aipyconfig file to ensure that the data is not out of the intranet throughout the entire process. Hardware support for NVIDIA GPU acceleration, when processing GB-sized Parquet files still maintain a second response.
Compared with cloud-based data analysis tools, AiPy's localized solution has three advantages: first, the raw data is always retained on the user's device; second, it supports configuration-free invocation of enterprise private APIs; and third, it can be customized with AST rules to strengthen the security review of the code. Test data from a tertiary hospital shows that when using locally deployed AiPy to process patient examination reports, the risk of data leakage is reduced to zero, and the analysis efficiency is increased by 3 times.
This answer comes from the articleAiPy: automating the task of running Python code for data analysisThe
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