# 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**](/developers/platform/get-started-in-less-than-10-minutes.md) - Fastest way to integrate Fiddler * **Agentic Monitoring Quick Starts** - Monitor AI agents and multi-step workflows * [LangGraph SDK Quick Start](/developers/agentic-ai-monitoring/langgraph-sdk-quick-start.md) or * [Strands Agent SDK Quick Start](/developers/agentic-ai-monitoring/strands-agent-quick-start.md) or * [OpenTelemetry Quick Start](/developers/agentic-ai-monitoring/opentelemetry-quick-start.md) * [**Experiments Quick Start**](/developers/experiments/experiments-quick-start.md) - Evaluate LLM outputs with custom metrics * [**Guardrails Quick Start**](/developers/guardrails/guardrails-quick-start.md) - 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](/developers/tutorials/experiments/rag-health-metrics-tutorial.md) * [Evals SDK Advanced Guide](/developers/tutorials/experiments/evals-sdk-advanced.md) * [Advanced Prompt Specs](/developers/tutorials/experiments/prompt-specs-advanced.md) ### Agentic & LLM Monitoring Monitor production LLM applications and AI agents: * [LangGraph SDK Quick Start](/developers/agentic-ai-monitoring/langgraph-sdk-quick-start.md) * [LangGraph SDK Advanced](/developers/tutorials/llm-monitoring/langgraph-sdk-advanced.md) * [Simple LLM Monitoring](/developers/llm-monitoring/simple-llm-monitoring.md) ### Guardrails Implement safety controls for your AI applications: * [Faithfulness Guardrails](/developers/tutorials/guardrails/guardrails-faithfulness.md) * [Safety Guardrails](/developers/tutorials/guardrails/guardrails-safety.md) * [PII Detection Guardrails](/developers/tutorials/guardrails/guardrails-pii.md) ### ML Monitoring Monitor traditional ML models in production: * [ML Monitoring Quick Start](/developers/ml-monitoring/simple-ml-monitoring.md) * [NLP Model Monitoring](/developers/tutorials/ml-monitoring/simple-nlp-monitoring-quick-start.md) * [Class Imbalance Handling](/developers/tutorials/ml-monitoring/class-imbalance-monitoring-example.md) * [Model Versions](/developers/tutorials/ml-monitoring/ml-monitoring-model-versions.md) * [Ranking Models](/developers/tutorials/ml-monitoring/ranking-model.md) * [Regression Models](/developers/tutorials/ml-monitoring/ml-monitoring-regression.md) * [Feature Impact Analysis](/developers/tutorials/ml-monitoring/user-defined-feature-impact.md) * [Computer Vision Monitoring](/developers/tutorials/ml-monitoring/cv-monitoring.md) ## 🍳 Cookbooks Use-case oriented guides that demonstrate end-to-end workflows for solving real problems: * [**RAG Evaluation Fundamentals**](/developers/cookbooks/rag-evaluation-fundamentals.md) — Evaluate RAG quality with built-in evaluators * [**Running RAG Experiments at Scale**](/developers/cookbooks/rag-experiments-at-scale.md) — Compare pipeline configurations systematically * [**Building Custom Judge Evaluators**](/developers/cookbooks/custom-judge-evaluators.md) — Create domain-specific evaluation criteria * [**Detecting Hallucinations in RAG**](/developers/cookbooks/hallucination-detection-pipeline.md) — Monitor for hallucinations in production * [**Monitoring Agentic Content Generation**](/developers/cookbooks/agentic-content-generation.md) — Quality and brand compliance for content agents ## 📖 Client Library Reference Comprehensive reference documentation for Fiddler's Python client: ### Getting Started * [Installation and Setup](/developers/client-library-reference/installation-and-setup.md) * [Naming Convention Guidelines](/developers/client-library-reference/naming-convention-guidelines.md) * [Alerts with Fiddler Client](/developers/client-library-reference/alerts-with-fiddler-client.md) ### Model Onboarding * [Create a Project and Model](/developers/client-library-reference/model-onboarding/create-a-project-and-model.md) * [Customizing Your Model Schema](/developers/client-library-reference/model-onboarding/customizing-your-model-schema.md) * [Task Types](/developers/client-library-reference/model-onboarding/task-types.md) * [Custom Missing Values](/developers/client-library-reference/model-onboarding/specifying-custom-missing-value-representations.md) ### Publishing Production Data * [Creating a Baseline Dataset](/developers/client-library-reference/publishing-production-data/creating-a-baseline-dataset.md) * [Publishing Batches of Events](/developers/client-library-reference/publishing-production-data/publishing-batches-of-events.md) * [Streaming Live Events](/developers/client-library-reference/publishing-production-data/streaming-live-events.md) * [Updating Events](/developers/client-library-reference/publishing-production-data/updating-events.md) * [Deleting Events](/developers/client-library-reference/publishing-production-data/deleting-events.md) * [Ranking Events](/developers/client-library-reference/publishing-production-data/ranking-events.md) **Model Task Examples:** * [Binary Classification](/developers/client-library-reference/explainability/model-task-examples/binary-classification.md) * [Multiclass Classification](/developers/client-library-reference/explainability/model-task-examples/multiclass-classification.md) * [Regression](/developers/client-library-reference/explainability/model-task-examples/regression.md) * [Ranking Model](/developers/client-library-reference/explainability/model-task-examples/uploading-a-ranking-model-artifact.md) **ML Framework Examples:** * [Scikit-learn](/developers/client-library-reference/explainability/ml-framework-examples/scikit-learn.md) * [TensorFlow HDF5](/developers/client-library-reference/explainability/ml-framework-examples/tensorflow-hdf5.md) * [TensorFlow SavedModel](/developers/client-library-reference/explainability/ml-framework-examples/tensorflow-savedmodel.md) * [XGBoost](/developers/client-library-reference/explainability/ml-framework-examples/xgboost.md) *** ## Related Documentation * [**SDK & API Reference**](/api/sdk-and-api-reference/readme.md) - Complete API documentation * [**Integrations**](https://docs.fiddler.ai/integrations/) - Connect Fiddler with your ML stack * [**Documentation**](/welcome/readme.md) - Product guides and platform documentation --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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