This blog post describes a compact, end-to-end prototype that reads a view from SAP Datasphere into a Jupyter notebook and analyzes each row with a local LLM (Meta Llama 3.1) , returning clean JSON you can filter and display. The same prompt pattern can be redirected to managed inference (Databricks, Hugging Face, or SAP AI Core) when you need speed and scale. This blog post describes a compact, end-to-end prototype that reads a view from SAP Datasphere into a Jupyter notebook and analyzes each row with a local LLM (Meta Llama 3.1) , returning clean JSON you can filter and display. The same prompt pattern can be redirected to managed inference (Databricks, Hugging Face, or SAP AI Core) when you need speed and scale.
This blog post describes a compact, end-to-end prototype that reads a view from SAP Datasphere into a Jupyter notebook and analyzes each row with a local LLM (Meta Llama 3.1) , returning clean JSON you can filter and display. The same prompt pattern can be redirected to managed inference (Databricks, Hugging Face, or SAP AI Core) when you need speed and scale. This blog post describes a compact, end-to-end prototype that reads a view from SAP Datasphere into a Jupyter notebook and analyzes each row with a local LLM (Meta Llama 3.1) , returning clean JSON you can filter and display. The same prompt pattern can be redirected to managed inference (Databricks, Hugging Face, or SAP AI Core) when you need speed and scale. Read More Technology Blog Posts by Members articles
#SAP
#SAPTechnologyblog
