AI and LLM Observability

Observability in AI and LLM systems is crucial for ensuring their reliability, performance, and compliance. This guide covers the key aspects of observability, including logging, monitoring, and alerting, to help you maintain and optimize your AI and LLM deployments.

Logging and Monitoring

• Request and Response Logging: Capture detailed logs of inputs and outputs for each query.
• Usage Metrics: Track metrics such as the number of requests, latency, and error rates.
• Performance Monitoring: Monitor CPU, GPU, memory usage, and other hardware metrics during model inference.

Request and Response Logging Guide

Tracing

• Request Tracing: Follow the path of a request through the system, including preprocessing and postprocessing steps.
• Latency Analysis: Identify bottlenecks and latency contributors in the inference pipeline.

Alerting and Notification

• Error Alerts: Set up alerts for critical errors or performance degradation.
• Threshold Alerts: Configure alerts for metrics that exceed predefined thresholds, such as number of requests or policy violations.

This feature is coming soon to beta

Telemetry Data

• Model Performance: Collect telemetry data on model performance, including accuracy, precision, recall, and F1 score over time.
• Prompt Efficiency: Understand how well your prompts are performing.

This feature is coming soon

Data Auditing and Versioning

• Model Versioning: Track different versions of the model and the changes between them.
• Model Drift Detection: Continuously monitor and detect shifts in model performance over time to ensure ongoing accuracy and reliability.
• Bias and Fairness Audits: Regularly audit models for biases and ensure they meet fairness standards.

Moderation and Compliance Guide / Request and Response Logging

Security and Compliance Monitoring

• Access Logs: Monitor who is accessing the model and what queries are being run.
• Compliance Checks: Ensure that model usage adheres to relevant regulations and standards, such as GDPR or HIPAA.

Moderation and Compliance Guide

Usage Analytics

• Pattern Recognition: Identify common patterns and trends in model usage.
• User Behavior: Analyze user interactions to improve model performance and user satisfaction.

Explainability and Interpretability

• Explainable AI Tools: Integrate tools that provide insights into model decisions, such as SHAP or LIME.
• Output Analysis: Analyze and log attention weights, feature importances, and other model internals contributing to the final output.

This feature is coming soon

These features provide a comprehensive observability framework to ensure that LLMs on the UsageGuard platform operate efficiently, accurately, and securely.