# Reference

- [Administration](/reference/settings.md): Dive into our guide to application settings in Fiddler. Learn to use the settings page to manage team setup, permissions, and credentials.
- [AWS VPC Endpoint Setup](/reference/settings/aws-vpc-endpoint-setup.md): Automated script to create AWS VPC endpoints for secure communication with Fiddler Cloud using AWS Virtual PrivateLink.
- [AWS Virtual PrivateLink Setup](/reference/settings/aws-vpl-setup.md): Step-by-step guide to configure AWS Virtual PrivateLink for secure communication between your AWS VPC and Fiddler Cloud.
- [Supported Browsers](/reference/settings/supported-browsers.md): Discover our product guide on supported web browsers for accessing Fiddler, including Google Chrome, Firefox, Safari, and Microsoft Edge.
- [LLM Gateway](/reference/settings/llm-gateway.md): Configure LLM provider credentials to enable AI-powered features in Fiddler using your own API keys from OpenAI, Anthropic, Gemini, and other providers.
- [Access Control](/reference/access-control.md): Explore our guides on authentication options with leading IDPs like Okta and Ping. Dive deep into authorization topics using the Fiddler UI.
- [Authentication Management](/reference/access-control/authn-authentication-management-console.md)
- [Email Login](/reference/access-control/email-login.md): This page documents the details of Fiddler's native email-based authentication including user account creation and password policy.
- [SSO Authentication Guide](/reference/access-control/sso-authentication-guide.md): Configure Single Sign-On authentication for Fiddler with Okta, Azure AD, Google, Ping, and others. Complete setup guide with troubleshooting tips.
- [Google SSO](/reference/access-control/google-integration.md): Learn how to configure Fiddler with Google for seamless Single Sign-On (SSO) authentication.
- [Microsoft Entra ID OIDC](/reference/access-control/single-sign-on-with-azure-ad.md): Learn to integrate Fiddler and Microsoft Entra ID, formerly known as Azure AD, for seamless Single Sign-On (SS0).
- [Okta Integration](/reference/access-control/okta-integration.md): Learn how to configure Fiddler with Okta for seamless Single Sign-On (SSO) authentication.
- [Okta SAML](/reference/access-control/okta-integration-saml.md): Learn how to configure Fiddler with Okta using SAML for seamless Single Sign-On (SSO) authentication.
- [Ping Identity SAML](/reference/access-control/ping-identity-saml.md): Learn how to configure Fiddler with Ping for seamless Single Sign-On (SSO) authentication.
- [Role-Based Access Control](/reference/access-control/role-based-access.md): Learn how Fiddler uses role-based access control with resources and roles. Discover how to manage access with resources, roles, and permissions in your company.
- [Mapping IdP Groups to Teams](/reference/access-control/mapping-ad-groups-to-fiddler-teams.md): This document describes the naming convention and rules for mapping internal AD groups to Fiddler Teams automatically.
- [Feature Maturity Definitions](/reference/feature-maturity-definitions.md): Review Fiddler's release and support policies for product features at different stages of maturity.
- [ML Metrics Reference](/reference/ml-metrics-reference.md): Complete reference of all built-in ML metrics supported by the Fiddler monitoring platform, organized by category and model task type.
- [LLM Observability Metrics Reference](/reference/llm-observability-metrics.md): Complete reference of all LLM observability metrics and enrichments supported by the Fiddler monitoring platform.
- [Glossary](/reference/glossary.md): Review product concepts and terminology for the Fiddler platform to help get up to speed quickly when adopting Fiddler for your ML and GenAI monitoring.
- [Agentic Observability](/reference/glossary/agentic-observability.md): Comprehensive monitoring, tracing, and analysis of AI agent systems that provides hierarchical visibility into agent reasoning, coordination, and decision-making across distributed multi-agent applica
- [Baseline](/reference/glossary/baseline.md): Reference datasets in Fiddler that serve as comparison points for detecting data drift, evaluating model performance, and identifying when production data deviates from expected patterns.
- [Custom Metric](/reference/glossary/custom-metrics.md): User-defined calculations in Fiddler that extend monitoring beyond standard metrics, allowing teams to track business-specific KPIs and specialized measurements for their AI applications.
- [Data Drift](/reference/glossary/data-drift.md): The statistical change in data distributions over time that can impact model performance. Fiddler detects drift by comparing production data against baselines to identify degradation causes.
- [Embedding Visualization](/reference/glossary/embedding-visualization.md): Interactive visualizations in Fiddler AI that transform complex embedding vectors into 3D displays, revealing semantic patterns, clusters, and outliers in LLM data.
- [Enrichment](/reference/glossary/enrichment.md): Comprehensive overview of enrichments in AI monitoring and evaluation. Learn how Fiddler's enrichment framework transforms raw LLM data into actionable insights through specialized metrics and custom
- [Experiments](/reference/glossary/experiments.md): Systematic assessment of LLM application quality through structured testing with datasets, evaluators, and experiments that enable data-driven decision-making for prompt optimization, model selection,
- [Fiddler Guardrails](/reference/glossary/guardrails.md): Explore the Fiddler's administrative features and settings available to Org Admins.
- [Fiddler Trust Service](/reference/glossary/trust-service.md): A specialized infrastructure that hosts purpose-built LLMs for evaluation, powering both monitoring metrics and real-time guardrails with higher efficiency than general-purpose models.
- [LLM Observability](/reference/glossary/llm-observability.md): Comprehensive monitoring of LLM applications that evaluates safety, quality, and performance metrics to detect issues like hallucinations, toxicity, and drift in generative AI systems.
- [Metric](/reference/glossary/metric.md): Metrics in Fiddler AI are quantitative measurements that evaluate model behavior, data quality, and performance over time, enabling proactive monitoring and issue detection.
- [Model Drift](/reference/glossary/model-drift.md): Changes in model performance over time due to shifting data patterns, concept evolution, or system degradation. Fiddler detects and diagnoses model drift to maintain AI reliability.
- [Model Performance](/reference/glossary/model-performance.md): "Quantitative evaluation of AI model accuracy and effectiveness in production. Fiddler tracks performance metrics over time to detect degradation and identify opportunities for improvement.
- [ML Observability](/reference/glossary/ml-observability.md): A comprehensive approach to monitoring AI systems that goes beyond performance metrics to provide insights into model behavior, data quality, and root causes of issues throughout the ML lifecycle.
- [Trust Score](/reference/glossary/trust-score.md): Quantitative scores generated by Fiddler's enrichment processes that measure LLM output quality and safety. These numerical metrics enable monitoring, alerting, and real-time decision-making for AI go