Analyze Live Data | AWS-Azure DW | via Custom GPT & LLM Apps
Published: July 27, 2024
Query. Transform. Analyze. Chart. File Ops. Build ML Models.
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.
Background
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.
LinkedIn Post here: Build ML Model with ChatGPT
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 a client recently.
This Series
In this series, I'll show you how to do just that. I'll be working with a 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).
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.
Edit: Video available at my old blog
Upcoming Episodes
GPT-LLM Capability Demonstration Videos
- Voice Mode Interaction
- Data Transformations
- Data Analysis
- Table Operations
- Inter-Warehouse Operations: Across AWS & Azure
- Build ML Models
- Limitations, Caveats & Constraints
How-To Guides
With Codes / Schemas / Github Repos
With special focus on how to use GPTs to get all this done quickly and efficiently:
- FastAPI Server and Endpoints
- Custom GPT: Custom Actions / JSON schemas
- External LLM Apps: Build with Flowise AI. Rapid deploy to internet/intranet
- External LLM Apps: LLM options. Cost-Performance trade-offs
- External LLM Apps: Low-cost custom deployment of Open Source LLMs
- External LLM Apps: API Connections with Flowise Custom Tool and JavaScript functions
- Basic Security: LLM Injection / API Keys / IP Rules / Allowed Domains
- Access Controls and selective access
- Setting up MySQL Server on AWS & Azure, Installing phpMyAdmin for rapid prototyping