MLOps Debugger

Visual debugger for ML operations and deployment pipelines

🎯 What This Does

I spent 3 days debugging why my MLOps deployment was failing. Now I can see exactly what is happening in real-time.

MLOps Debugger is a visual debugging toolkit for ML operations that helps you:

ML deployments fail silently in production:

Challenge	Impact
Silent Failures	Models serve wrong predictions without errors
Pipeline Breaks	Data drift breaks downstream systems
No Visibility	Don't know why deployment failed
Slow Recovery	Hours spent finding root cause

pip install -r requirements.txt
streamlit run app.py

Scenario: Deploying new model version to production

Problem: Model serves predictions but accuracy dropped 40%

Solution: Visual pipeline tracer shows data preprocessing step failed silently

Value: Caught issue before customers affected

Scenario: Multi-step ML pipeline (extract → clean → train → deploy)

Problem: Pipeline fails randomly, no clear pattern

Solution: Real-time monitoring shows memory spike during training

Value: Optimized resource allocation, 90% fewer failures

Scenario: Production model monitoring

Problem: Model performance degrades over weeks

Solution: Automated drift detection with alerting

Value: Retrained model before significant business impact

Built by Anand • 26 years delivering production systems

Trend Research: Inspired by langchain, llama-index, open-webui

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