See me talking to Custom GPT in voice mode. At the backend, it is talking to Azure Data Warehouse (MySQL). ... and analyzing profile of campaign responders ..
... am inserting conditional fields, asking questions about data, creating distributions based on calculated fields, and generating charts.
Not in the video, but I have also carried out a bunch of other tasks in voice mode: creating summary tables, merging summary tables back with modeling datasets, appending tables, dropping tables, etc.
๐๐ฝ๐ฝ๐น๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป๐
Senior Leadership Voice Dashboards / Adhoc query support / Quick Queries & Charts / Rapid data transformations / Intelligent IVR / Employee queries ... numerous applications ..ย
This is Part-2 of my series on connecting to and analyzing data on live data warehouses on AWS & Azure via Custom GPT and LLM Apps.
Link to Part-1
[DW Series -Part 01] ๐๐ป๐ฎ๐น๐๐๐ฒ ๐๐ถ๐๐ฒ ๐๐ฎ๐๐ฎ | ๐๐ช๐ฆโ๐๐๐๐ฟ๐ฒ ๐๐ช | ๐๐ถ๐ฎ ๐๐๐๐๐ผ๐บ ๐๐ฃ๐ง & ๐๐๐ ๐๐ฝ๐ฝ๐
Part-01: Lighthearted Introduction.
๐จ๐ฃ๐๐ข๐ ๐๐ก๐ ๐๐ฃ๐๐ฆ๐ข๐๐๐ฆ : ๐๐ข๐ ๐๐ก๐ ๐ก๐๐ซ๐ง
๐๐ฃ๐ง-๐๐๐ ๐๐ฎ๐ฝ๐ฎ๐ฏ๐ถ๐น๐ถ๐๐ ๐๐ฒ๐บ๐ผ๐ป๐๐๐ฟ๐ฎ๐๐ถ๐ผ๐ป ๐ฉ๐ถ๐ฑ๐ฒ๐ผ๐ย
Data transformations, analysis, charts, table operations, inter-warehouse operations, operating on large data sets, ML model build.....and limitations, caveats & constraints
๐๐ผ๐-๐ง๐ผ ๐๐๐ถ๐ฑ๐ฒ๐ย
With Codes / Schemas / GitHub Repos
Connecting to data warehouses, deploying FastAPI Server, GPT action schemas, deploying on external LLM Apps, security issues, LLM cost & options, prototype warehouse setup on AWS & Azure.
๐ ๐ช๐ถ๐๐ต ๐๐ฝ๐ฒ๐ฐ๐ถ๐ฎ๐น ๐ณ๐ผ๐ฐ๐๐ ๐ผ๐ป ๐ต๐ผ๐ ๐๐ผ ๐๐๐ฒ ๐๐ฃ๐ง๐ ๐๐ผ ๐ด๐ฒ๐ ๐ฎ๐น๐น ๐๐ต๐ถ๐ ๐ฑ๐ผ๐ป๐ฒ ๐พ๐๐ถ๐ฐ๐ธ๐น๐ ๐ฎ๐ป๐ฑ ๐ฒ๐ณ๐ณ๐ถ๐ฐ๐ถ๐ฒ๐ป๐๐น๐