AI Pipeline
End-to-end AI pipeline for operational insights
Built a local-first AI pipeline observability project that ingests run logs, flags failures and runtime spikes, and surfaces pipeline health through CLI reporting and a lightweight dashboard.
Stack
Python, DuckDB, Streamlit, Workflow Monitoring, CLI Reporting
Impact
Turned an abstract project idea into a runnable MVP with ingestion, anomaly detection, CLI summaries, and a local dashboard for pipeline health.
End-to-end AI pipeline for operational insights
This project is now a small but real local-first observability layer for AI and data workflows.
What exists now
- JSON ingestion of sample pipeline runs into DuckDB
- CLI reporting for run history and alert summaries
- anomaly detection for failure rate and duration spikes
- a lightweight local Streamlit dashboard for visual inspection
- saved text report output for generated run summaries
Why it matters
The point is not a flashy model demo. It is showing how AI/data workflows become more useful when pipeline health, failures, and strange runtime behavior are visible instead of hidden.
What this project shows
- Python + DuckDB pipeline thinking
- local-first observability design
- monitoring mindset around AI/data workflows
- ability to turn an abstract project idea into a runnable MVP
Current status
This is an in-progress portfolio build, not a polished production tool yet. But it now has real repo structure, a working CLI flow, and a viewable dashboard instead of being just a placeholder concept.