Overview

Practical guides, tutorials, and reference documentation for building with Fiddler.

⚡ Quick Starts

Get up and running in minutes with step-by-step quick start guides:

• Get Started in <10 Minutes - Fastest way to integrate Fiddler

• Agentic Monitoring Quick Starts - Monitor AI agents and multi-step workflows

  • LangGraph SDK Quick Start or

  • Strands Agent SDK Quick Start or

  • OpenTelemetry Quick Start

• Experiments Quick Start - Evaluate LLM outputs with custom metrics

• Guardrails Quick Start - Add safety guardrails to your AI applications

📚 Tutorials

In-depth, hands-on tutorials organized by product area:

Experiments

Learn how to evaluate and test your LLM applications:

• RAG Health Metrics Tutorial

• Evals SDK Advanced Guide

• Advanced Prompt Specs

Agentic & LLM Monitoring

Monitor production LLM applications and AI agents:

• LangGraph SDK Quick Start

• LangGraph SDK Advanced

• Simple LLM Monitoring

Guardrails

Implement safety controls for your AI applications:

• Faithfulness Guardrails

• Safety Guardrails

• PII Detection Guardrails

ML Monitoring

Monitor traditional ML models in production:

• ML Monitoring Quick Start

• NLP Model Monitoring

• Class Imbalance Handling

• Model Versions

• Ranking Models

• Regression Models

• Feature Impact Analysis

• Computer Vision Monitoring

🍳 Cookbooks

Use-case oriented guides that demonstrate end-to-end workflows for solving real problems:

• RAG Evaluation Fundamentals — Evaluate RAG quality with built-in evaluators

• Running RAG Experiments at Scale — Compare pipeline configurations systematically

• Building Custom Judge Evaluators — Create domain-specific evaluation criteria

• Detecting Hallucinations in RAG — Monitor for hallucinations in production

• Monitoring Agentic Content Generation — Quality and brand compliance for content agents

📖 Client Library Reference

Comprehensive reference documentation for Fiddler's Python client:

Getting Started

• Installation and Setup

• Naming Convention Guidelines

• Alerts with Fiddler Client

Model Onboarding

• Create a Project and Model

• Customizing Your Model Schema

• Task Types

• Custom Missing Values

Publishing Production Data

• Creating a Baseline Dataset

• Publishing Batches of Events

• Streaming Live Events

• Updating Events

• Deleting Events

• Ranking Events

Model Task Examples:

• Binary Classification

• Multiclass Classification

• Regression

• Ranking Model

ML Framework Examples:

• Scikit-learn

• TensorFlow HDF5

• TensorFlow SavedModel

• XGBoost

---

Related Documentation

• SDK & API Reference - Complete API documentation

• Integrations - Connect Fiddler with your ML stack

• Documentation - Product guides and platform documentation