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Solve problems, and share tips and tricks with other JMP users.
Hey everyone,
I wanted to share a highly efficient workflow I've developed that bridges JMP's data manipulation capabilities with the power of NVIDIA's latest AI models running on their free API platform (NIM).
The core idea is to automate the generation of executable Python/JSL scripts directly within JMP, effectively creating a "content factory" for data analysis tasks.
The Technical Workflow:
Data Ingestion: I start by defining specific analytical topics or queries directly in a JMP data table (e.g., "Philosophical Logic of Journey to the West," "Cultural Background of Dream of the Red Chamber").
AI Orchestration: A Python script embedded in JMP handles the heavy lifting. It calls NVIDIA's API (using the deepseek-ai/deepseek-v4-pro model via NIM) to process these queries.
Code Synthesis: Instead of just returning text summaries, the model is prompted to generate structured Python code snippets designed to work seamlessly with JMP's Graph Builder and other statistical tools.
Automated Output: The generated Python code is saved as a local file or directly executed, allowing for instant visualization and further statistical analysis without manual coding.
Key Benefits Observed:
Blazing Speed: Leveraging NVIDIA's optimized inference stack, the API responds incredibly fast (processing multiple complex queries in seconds).
Mass Production: This method allows for batch processing. You can feed hundreds of analytical intents into the system and receive a full set of ready-to-run scripts automatically.
Closed-Loop Automation: It creates a seamless loop from natural language input -> AI interpretation -> executable code -> visual output, all without leaving the JMP environment.
This setup has dramatically cut down the time needed for exploratory data analysis, especially when dealing with qualitative data or complex comparative studies. Has anyone else experimented with integrating NVIDIA NIM or similar large models directly into their JMP scripting workflows?
Thanks!
