Arize AI Phoenix

➺ PlatForm Features:

  • Model monitoring
  • Performance tracking
  • Drift detection
  • Root cause analysis 

➺ Technical Components:

Monitoring Capabilities 
  • Response quality
  • Latency tracking
  • Error detection
  • Resource usage 

    • ➺ Analysis Tools:

  • Performance analytics
  • Drift visualization
  • Error analysis
  • Usage patterns 

    • ➺ Integration Features:

  • API endpoints
  • Dashboard access
  • Alert system
  • Report generation 

    • ➺ Implementation Guide:

    python

    # Arize Phoenix setup from arize.phoenix import Client

    client = Client()

    # Log prediction
    client.log_prediction(
    model_id=”llm-v1″,
    prediction=response,
    features=input_data,
    metrics={
    “latency”: response_time,
    “tokens”: token_count
    }
    )

    ➺ Key Benefits:

  • Real-time monitoring
  • Automated testing
  • Quality assurance
  • Performance optimization 
    • Each framework offers unique capabilities for LLM evaluation, and they can be used individually or in combination depending on your specific needs