AI-Powered Data Quality & Incident Automation Using Python and Llama 3.2

1. 1Build a data quality checker using Python and Llama 3.2 to analyze data for inconsistencies
2. 2Configure a local LLM analysis pipeline to identify potential issues
3. 3Create a SQLite database to store incident reports and data quality metrics
4. 4Develop a FastAPI application to automate incident reporting and data quality checks
5. 5Test the automated data quality checks and incident reporting pipeline using sample data

Data scientists and engineers can benefit from this project by automating data quality checks and incident reporting, improving overall data reliability and reducing manual effort

💡 Automating data quality checks and incident reporting can significantly improve data reliability and reduce manual effort

🚀 Automate data quality checks and incident reporting with Python, Llama 3.2, SQLite, and FastAPI! 🚀