LogoLogo
👨‍💻 API Reference📣 Release Notes📺 Request a Demo
  • Introduction to Fiddler
    • Monitor, Analyze, and Protect your ML Models and Gen AI Applications
  • Fiddler Doc Chatbot
  • First Steps
    • Getting Started With Fiddler Guardrails
    • Getting Started with LLM Monitoring
    • Getting Started with ML Model Observability
  • Tutorials & Quick Starts
    • LLM and GenAI
      • LLM Evaluation - Compare Outputs
      • LLM Monitoring - Simple
    • Fiddler Free Guardrails
      • Guardrails - Quick Start Guide
      • Guardrails - Faithfulness
      • Guardrails - Safety
      • Guardrails FAQ
    • ML Observability
      • ML Monitoring - Simple
      • ML Monitoring - NLP Inputs
      • ML Monitoring - Class Imbalance
      • ML Monitoring - Model Versions
      • ML Monitoring - Ranking
      • ML Monitoring - Regression
      • ML Monitoring - Feature Impact
      • ML Monitoring - CV Inputs
  • Glossary
    • Product Concepts
      • Baseline
      • Custom Metric
      • Data Drift
      • Embedding Visualization
      • Fiddler Guardrails
      • Fiddler Trust Service
      • LLM and GenAI Observability
      • Metric
      • Model Drift
      • Model Performance
      • ML Observability
      • Trust Score
  • Product Guide
    • LLM Application Monitoring & Protection
      • LLM-Based Metrics
      • Embedding Visualizations for LLM Monitoring and Analysis
      • Selecting Enrichments
      • Enrichments (Private Preview)
      • Guardrails for Proactive Application Protection
    • Optimize Your ML Models and LLMs with Fiddler's Comprehensive Monitoring
      • Alerts
      • Package-Based Alerts (Private Preview)
      • Class Imbalanced Data
      • Enhance ML and LLM Insights with Custom Metrics
      • Data Drift: Monitor Model Performance Changes with Fiddler's Insights
      • Ensuring Data Integrity in ML Models And LLMs
      • Embedding Visualization With UMAP
      • Fiddler Query Language
      • Model Versions
      • How to Effectively Use the Monitoring Chart UI
      • Performance Tracking
      • Model Segments: Analyze Cohorts for Performance Insights and Bias Detection
      • Statistics
      • Monitoring ML Model and LLM Traffic
      • Vector Monitoring
    • Enhance Model Insights with Fiddler's Slice and Explain
      • Events Table in RCA
      • Feature Analytics Creation
      • Metric Card Creation
      • Performance Charts Creation
      • Performance Charts Visualization
    • Master AI Monitoring: Create, Customize, and Compare Dashboards
      • Creating Dashboards
      • Dashboard Interactions
      • Dashboard Utilities
    • Adding and Editing Models in the UI
      • Model Editor UI
      • Model Schema Editing Guide
    • Fairness
    • Explainability
      • Model: Artifacts, Package, Surrogate
      • Global Explainability: Visualize Feature Impact and Importance in Fiddler
      • Point Explainability
      • Flexible Model Deployment
        • On Prem Manual Flexible Model Deployment XAI
  • Technical Reference
    • Python Client API Reference
    • Python Client Guides
      • Installation and Setup
      • Model Onboarding
        • Create a Project and Onboard a Model for Observation
        • Model Task Types
        • Customizing your Model Schema
        • Specifying Custom Missing Value Representations
      • Publishing Inference Data
        • Creating a Baseline Dataset
        • Publishing Batches Of Events
        • Publishing Ranking Events
        • Streaming Live Events
        • Updating Already Published Events
        • Deleting Events From Fiddler
      • Creating and Managing Alerts
      • Explainability Examples
        • Adding a Surrogate Model
        • Uploading Model Artifacts
        • Updating Model Artifacts
        • ML Framework Examples
          • Scikit Learn
          • Tensorflow HDF5
          • Tensorflow SavedModel
          • XGBoost
        • Model Task Examples
          • Binary Classification
          • Multiclass Classification
          • Regression
          • Uploading A Ranking Model Artifact
      • Naming Convention Guidelines
    • Integrations
      • Data Pipeline Integrations
        • Airflow Integration
        • BigQuery Integration
        • Integration With S3
        • Kafka Integration
        • Sagemaker Integration
        • Snowflake Integration
      • ML Platform Integrations
        • Integrate Fiddler with Databricks for Model Monitoring and Explainability
        • Datadog Integration
        • ML Flow Integration
      • Alerting Integrations
        • PagerDuty Integration
    • Comprehensive REST API Reference
      • Projects REST API Guide
      • Model REST API Guide
      • File Upload REST API Guide
      • Custom Metrics REST API Guide
      • Segments REST API Guide
      • Baselines REST API Guide
      • Jobs REST API Guide
      • Alert Rules REST API Guide
      • Environments REST API Guide
      • Explainability REST API Guide
      • Server Info REST API Guide
      • Events REST API Guide
      • Fiddler Trust Service REST API Guide
    • Fiddler Free Guardrails Documentation
  • Configuration Guide
    • Authentication & Authorization
      • Adding Users
      • Overview of Role-Based Access Control
      • Email Authentication
      • Okta OIDC SSO Integration
      • Azure AD OIDC SSO Integration
      • Ping Identity SAML SSO Integration
      • Google OIDC SSO Integration
      • Mapping LDAP Groups & Users to Fiddler Teams
    • Application Settings
    • Supported Browsers
  • History
    • Release Notes
    • Python Client History
    • Compatibility Matrix
    • Product Maturity Definitions
Powered by GitBook

© 2024 Fiddler Labs, Inc.

On this page
  • Creating Dashboards
  • Add Monitoring Charts
  • Dashboard Filters
  • Model Comparison

Was this helpful?

  1. Product Guide
  2. Master AI Monitoring: Create, Customize, and Compare Dashboards

Creating Dashboards

PreviousMaster AI Monitoring: Create, Customize, and Compare DashboardsNextDashboard Interactions

Last updated 14 days ago

Was this helpful?

Creating Dashboards

To begin using our dashboard feature, navigate to the dashboard page by clicking on "Dashboards" from the top-level navigation bar. On the Dashboards page, you can choose to either select from previously created dashboards or create a new one. This simple process allows you to quickly access your dashboards and begin monitoring your models' performance, data drift, data integrity, and traffic.

When creating a new dashboard, it's important to note that each dashboard is tied to a specific project space. This means that only models and charts associated with that project can be added to the dashboard. To ensure you're working within the correct project space, select the desired project space before entering the dashboard editor page, then click "Continue." This will ensure that you can add relevant charts and models to your dashboard.

Auto-Generated Dashboards

Fiddler will automatically generated model monitoring dashboards for all models registered to the platform. Depending on the task type, these dashboards will include charts spanning from Performance, Traffic, Drift, and Data Integrity metrics.

Auto-generated model monitoring dashboard

Accessing the default dashboard from the model schema page via Insights

Add Monitoring Charts

Once you’ve created a dashboard, you can add previously saved monitoring charts that display these metrics over time, making it easy to track changes and identify patterns.

If you'd like to add an existing chart to your dashboard, select "Saved Charts" to display a full list of monitoring charts that are available in your project space. This makes it easy to quickly access and add the charts you need to your dashboard for monitoring and reporting purposes.

To further customize your dashboard, you can select the saved monitoring charts of interest by clicking on their respective cards. For instance, you might choose to add charts for Accuracy, Drift, Traffic, and Range Violation to your dashboard for a more comprehensive model overview. By adding these charts to your dashboard, you can quickly access important metrics and visualize your model's performance over time, enabling you to identify trends and patterns that might require further investigation.

Dashboard Filters

There are three main filters that can be applied to all the charts within dashboards, these include date range, time zone, and bin size.

Date Range

When the Default time range is selected, the data range, time zone, and bin size that each monitoring chart was originally saved with will be applied. This enables you to create a dashboard where each chart shows a unique filter set to highlight what matters to each team. Updating the date range will unlock the time zone and bin size filters. You can select from a number of predefined ranges or choose Custom to select a start and end date-time.

Bin Size

Bin size controls the frequency at which data is displayed on your monitoring charts. You can select from the following bin sizes: Hour, Day, Week, or Month.

📘 Note: Hour bin sizes are not supported for time ranges above 90 days.

For example, if we select the 6M data range, we see that the Hourly bin selection is disabled. This is disabled to avoid long computation and dashboard loading times.

Saved Model Updates

If you recently created or updated a saved chart and are not seeing the changes either on the dashboard itself or the Saved Charts list, click the refresh button on the main dashboard studio or within the saved charts list to reflect updates.

Model Comparison

With our dashboard feature, you can also compare multiple models side-by-side, making it easy to see which models are performing the best and which may require additional attention. To create model-to-model comparison dashboards, ensure the models you wish to compare belong to the same project. Create the desired charts for each model and then add them to a single dashboard. By creating a single dashboard that tracks the health of all of your models, you can save time and simplify your AI monitoring efforts. With these dashboards, you can easily share insights with your team, management, or stakeholders, and ensure that everyone is up-to-date on your AI performance.

Auto-generated dashboard are automatically set as the default dashboards for each model, and can be accessed via the Insights button on the Homepage or Model Schema pages, or alternatively all dashboards are always accessible in the Dashboards list page. Default dashboards and their charts can easily be modified to display the desired set of and visualizations to meet any use case.

To create a new monitoring chart for your dashboard, simply select "New Monitoring Chart" from the "Add" dropdown menu. For more information on creating and customizing monitoring charts, check out our .

Refer to our and pages for more info on dashboard usage.

monitoring
embedding
Monitoring Charts UI Guide
Dashboard Utilities
Dashboard Interactions
Auto-generated model monitoring dashboard
Accessing the default dashboard from the model schema page via Insights

Questions? to a product expert or a demo.

Need help? Contact us at .

❓
💡
Talk
request
help@fiddler.ai