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Fiddler Documentation
Product DocumentationRecipesAPI ReferenceRelease Notes
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Product DocumentationRecipesAPI ReferenceRelease Notes

Overview

  • Welcome to Fiddler's Documentation!
  • Product Tour

DOC CHATBOT

  • Fiddler Docs Chat

QuickStart Notebooks

  • Simple Monitoring
  • NLP Monitoring
  • CV Monitoring
  • Explainability with a Surrogate Model
  • Explainability with Model Artifact
  • Class Imbalance Monitoring Example
  • Ranking Monitoring Example

Illustrative Walkthroughs

  • Fraud Detection
  • Customer Churn Prediction

Platform Guide

  • Administration
  • Project Architecture
  • Model: Artifacts, Package, Surrogate
  • Monitoring
    • Alerts
    • Data Drift
    • Traffic
    • Data Integrity
    • Performance Tracking
    • Vector Monitoring
    • Class-Imbalanced Data
    • Monitoring Charts
  • Dashboards
  • Explainability
    • Point Explainability
    • Global Explainability
  • Fairness
  • Analytics and Evaluation
  • Model Task Types
  • Flexible Model Deployment
  • Baselines
  • Supported Browsers

UI Guide

  • Administration
    • Settings
    • Inviting Users
    • Project Structure on UI
    • Authorization and Access Control
  • Monitoring
    • Data Drift
    • Performance
    • Data Integrity
    • Traffic
    • Alerts with Fiddler UI
    • Monitoring Charts UI
  • Explainability
    • Point Explainability
    • Global Explainability
    • Surrogate Models
  • Analytics
    • Useful Queries for Root Cause Analysis
  • Evaluation
  • Fairness
  • Dashboards
    • Dashboard Utilities
    • Dashboard Interactions

Client Guide

  • Installation and Setup
  • Authorizing the Client
  • Designing a Baseline Dataset
  • Uploading a Baseline Dataset
  • Customizing Your Dataset Schema
  • Specifying Custom Missing Value Encodings
  • Onboarding a Model
    • Binary Classification
    • Multiclass Classification
    • Regression
    • Ranking
  • Surrogate Models - Client Guide
  • Uploading model artifacts
  • Updating model artifacts
  • Package.py Examples
    • Binary Classification Model Package.py
    • Regression Model Package.py
    • Multi-class Classification Model Package.py
    • Ranking Model Package.py
  • ML Framework Examples
    • Uploading a scikit-learn Model Artifact
    • Uploading an XGBoost Model Artifact
    • Uploading a TensorFlow SavedModel Model Artifact
    • Uploading a TensorFlow HDF5 Model Artifact
  • Publishing Production Data
    • Streaming Live Events
    • Publishing Batches of Events
    • Publishing Events With Complex Data Formats
    • Updating Events
    • Retrieving Events
    • Using Custom Timestamps
    • Publishing Ranking Events
  • Alerts with Fiddler Client

Deployment Guide

  • Deploying Fiddler
    • System Architecture
    • On-prem Technical Requirements
    • On-prem Installation Guide
    • Routing to Fiddler (on-prem)
  • Single Sign On with Okta

INTEGRATIONS

  • Data Pipeline Integrations
    • S3 Integration
    • Airflow Integration
    • SageMaker Integration
    • BigQuery Integration
    • Snowflake Integration
    • Kafka Integration
  • ML Platform Integrations
    • SageMaker ML Integration
    • Datadog Integration
    • Databricks Integration
    • ML Flow Integration
  • Alerting Integrations
    • PagerDuty Integration

Publishing Production Data

Suggest Edits

This Section guides you on the various ways you can provide event data to Fiddler and update and retrieve them.

Updated 10 months ago


What’s Next
  • Streaming Live Events
  • Publishing Batches of Events
  • Publishing Events With Complex Data Formats