Lighthearted Introduction
๐ค๐๐ฒ๐ฟ๐. ๐ง๐ฟ๐ฎ๐ป๐๐ณ๐ผ๐ฟ๐บ. ๐๐ป๐ฎ๐น๐๐๐ฒ. ๐๐ต๐ฎ๐ฟ๐. ๐๐ถ๐น๐ฒ ๐ข๐ฝ๐. ๐๐๐ถ๐น๐ฑ ๐ ๐ ๐ ๐ผ๐ฑ๐ฒ๐น๐
All in the Natural Language of your choice....
From within Custom GPT (ChatGPT Plus) as well as via externally deployed LLM apps on your intranet or public website...."
โก๏ธ Earlier this year, I published a video demonstrating how to build a machine learning model with ChatGPT Plus using natural language. That required an offline data upload.
โก๏ธ What if we could build ML models and perform analyses by directly connecting to live data warehouses in AWS and Azure?
....and not just the final analysis and model building, but also data transformations, modeling dataset creation, table level operations, record insertions, modifications, charts, and cross tabs. Pretty much anything you can do with Python/SQL, but with a simple UI and natural language.
I had to do something similar for client recently.
โก๏ธ In this series, I'll show you how to do just that. I'll be working with prototype data warehouse I set up in AWS (RDS-MySQL) and Azure (MySQL), with tables ranging from just a few records to millions (the largest table has 10 Million records).
โก๏ธ This is the kick-off video ....and a light-hearted introduction to connecting and working with AWS and Azure data warehouses via Custom GPT
Hope you have as much fun watching this video as I had making it.
-------------------------------------------
๐บ ๐จ๐ฃ๐๐ข๐ ๐๐ก๐ ๐๐ฃ๐๐ฆ๐ข๐๐๐ฆ : ๐๐ข๐ ๐๐ก๐ ๐ก๐๐ซ๐งย
๐๐ฃ๐ง-๐๐๐ ๐๐ฎ๐ฝ๐ฎ๐ฏ๐ถ๐น๐ถ๐๐ ๐๐ฒ๐บ๐ผ๐ป๐๐๐ฟ๐ฎ๐๐ถ๐ผ๐ป ๐ฉ๐ถ๐ฑ๐ฒ๐ผ๐
๐นVoice Mode Interactionย
๐นData Transformations
๐นData Analysis
๐นTable Operations
๐นInter-Warehouse Operations: Across AWS & Azureย
๐นBuild ML Models
๐นLimitations, Caveats & Constraints
๐๐ผ๐-๐ง๐ผ ๐๐๐ถ๐ฑ๐ฒ๐ย
With Codes / Schemas / Github Repos
๐ ๐ช๐ถ๐๐ต ๐๐ฝ๐ฒ๐ฐ๐ถ๐ฎ๐น ๐ณ๐ผ๐ฐ๐๐ ๐ผ๐ป ๐ต๐ผ๐ ๐๐ผ ๐๐๐ฒ ๐๐ฃ๐ง๐ ๐๐ผ ๐ด๐ฒ๐ ๐ฎ๐น๐น ๐๐ต๐ถ๐ ๐ฑ๐ผ๐ป๐ฒ ๐พ๐๐ถ๐ฐ๐ธ๐น๐ ๐ฎ๐ป๐ฑ ๐ฒ๐ณ๐ณ๐ถ๐ฐ๐ถ๐ฒ๐ป๐๐น๐
๐นFastAPI Server and Endpoints
๐นCustom GPT: Custom Actions / JSON schemas.
๐นExternal LLM Apps: Build with Flowise AI. Rapid deploy to internet/ intranet
๐นExternal LLM Apps: LLM options. Cost-Performance trade-offs
๐นExternal LLM Apps: Low-cost custom deployment of Open Source LLMs.
๐นExternal LLM Apps : API Connections with Flowise Custom Tool and JavaScript functions.
๐นBasic Security: LLM Injection / API Keys / IP Rules / Allowed Domains
๐นAccess Controls and selective access.
๐นSetting up MySQL Server on AWS & Azre, Installing phpMyAdmin for rapid prototyping