LLM-based System Observability Framework

An LLM-based System Observability Framework is an ai system observability framework that enables creation of llm-based system observability systems (to support llm system monitoring and evaluation).

• Context:
  • It can provide Model Performance Analysis through telemetry instrumentation.
  • It can enable System Evaluation through built-in metrics.
  • It can support Application Monitoring through trace collection.
  • It can maintain Quality Control through automated checks.
  • ...
  • It can (often) facilitate Framework Integration through standard apis.
  • It can (often) provide Development Support through testing environments.
  • It can (often) implement Resource Tracking through usage monitors.
  • It can (often) support Team Workflow through shared dashboards.
  • ...
  • It can range from being a Basic Monitoring Framework to being an Advanced Analytics Framework, depending on its feature set.
  • It can range from being a Development Tool Framework to being a Production Framework, depending on its deployment type.
  • ...
  • It can integrate with Machine Learning Frameworks for model evaluation.
  • It can connect to Development Platforms for workflow automation.
  • It can support Cloud Platforms for deployment flexibility.
  • ...
• Examples:
  • • Open Source Frameworks, such as:
      • Arize Phoenix Framework for experimental development.
      • Langfuse Framework for production monitoring.
    • Commercial Frameworks, such as:
      • Weights & Biases Framework for mlops workflows.
      • Deepchecks Framework for model validation.
  • ...
• Counter-Examples:
  • Traditional APM Frameworks, which lack llm-specific analysis.
  • Generic Monitoring Frameworks, which lack specialized llm features.
  • Development Testing Frameworks, which lack comprehensive observability.
• See: AI Observability Framework, LLM Testing Framework, Model Monitoring Framework, Development Framework, MLOps Framework.

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