Release Notes
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Updated Fiddler Fast Trust Faithfulness Model
Classification Improvements:
Improved accuracy on Q&A and simple knowledge-retrieval tasks
Enhanced accuracy on Q&A with longer contexts
Improved accuracy on "off-label" tasks like JSON-to-Text and Dialogue/Chat exchanges
Performance Boost:
15-20% faster processing on longer contexts
Enhanced Fiddler Fast Trust Safety Model
Increased Safety model context window from 4,000 to 800,000 tokens
Benefit: Enables comprehensive safety analysis on much larger documents and conversations without truncation
Multi-threading for Embedding Enrichment
Implemented parallel processing for embedding generation
Performance Impact: At least a 5x improvement in processing speed for embedding enrichments
Scalability: Can achieve even greater performance with additional threads and resources
Benefit: Significantly reduces processing time for high-volume LLM monitoring pipelines
Microsoft Teams Webhook Integration
Added native support for Microsoft Teams webhook notifications alongside existing Slack integration
Benefit: Teams can now receive alert notifications directly in their Microsoft Teams channels
How to use: Configure Microsoft Teams webhooks in the Webhook Integrations tab of the page
Impact: Streamlines communication workflows for organizations using Microsoft Teams as their primary collaboration platform
Security Updates: Applied routine security patches and version updates to application frameworks to stay up-to-date with the latest improvements.
Fixes and Security Updates
Security Updates: Applied routine security patches and version updates to application frameworks to stay up-to-date with the latest improvements.
Documentation Updates
Detailed explanations
Implementation guidance
Related resources and references
This enhancement makes it easier to understand key product terminology and concepts while providing deeper technical context when needed. We'll continue to add new terms to the glossary incrementally over time.
Benefit: Enables flexible classification tasks tailored to your specific business needs, going beyond pre-defined enrichment types
Security Updates: Applied routine security patches and version updates to application frameworks to stay up-to-date with the latest improvements.
Removed Deployment Guide: The Deployment Guide is no longer relevant to Fiddler SaaS and managed SaaS offerings.
Baseline Name Length: Increased the maximum allowed characters from 30 to 256. This change enables more descriptive baseline names for complex projects with multiple models and datasets.
Action Required: None. Existing baselines remain unchanged.
Enhanced Job Error Messages: Error messages during metrics aggregation now specifically identify which step in the data ingestion process failed, helping you troubleshoot pipeline issues faster.
Benefit: Reduces debugging time by pinpointing in which step jobs are failing.
Security Updates: Applied routine security patches to container images and application frameworks to address recent vulnerabilities.
Homepage Cache Timestamp: Fixed an issue where cached dashboard data would display incorrect "Last Updated" timestamps, leading to confusion about data freshness.
Streamlined Structure: Reorganized documentation with improved navigation paths between related topics.
New Section: Consolidated technical guides and API references into the "Technical Reference" section.
Enhanced Charts Framework: Implemented significant improvements to our charting system, delivering more consistent rendering and reliable performance across all dashboards and visualizations.
Performance Optimization: Updated application framework components to improve validation speed during model onboarding and data publishing processes.
Enhanced Error Handling: Redesigned validation messages during Baseline creation to provide more actionable and detailed troubleshooting guidance.
Concurrency Improvements: Optimized metrics calculation when model edits trigger recalculations, reducing processing time while preventing disruption to production data pipelines.
Streamlined Structure: Merged the former UI Guide into the Product Guide for a more intuitive navigation experience.
Expanded Content: Added comprehensive data publishing guides with practical examples and best practices for various data types and formats.
New Priority Queue for Streaming Data
We've added a dedicated queue for processing streaming inference data. This improvement gives streaming events priority handling compared to batch processing jobs.
Improvements
This release enhances the clarity of error messages when configuring different baseline types for a model.
How it helps you
Faster processing times for streaming data
Reduced latency for real-time monitoring applications
No delays from large batch upload operations
How It Works
Streaming events now flow through a separate, high-priority processing lane—similar to an HOV lane on a highway—bypassing any congestion from batch operations. This feature works automatically with your existing implementation. No configuration changes required.
Introducing Fiddler Guardrails!
Improvements
This release includes updates focused on improving system performance, stability, and scalability. These improvements ensure a smoother user experience and provide a more robust platform for future developments.
Guardrails Endpoint Change
Fast Safety Guardrails
ftl_prompt_safety
ftl-safety
Fast Faithfulness Guardrails
ftl_response_faithfulness
ftl-response-faithfulness
Introducing UI Model Onboarding, a powerful new capability that enables teams to onboard models directly through the Fiddler user interface. This streamlined approach to model integration enhances the platform's accessibility while maintaining robust monitoring capabilities.
Here's what this means for you:
Easy to use: We designed the Model Onboarding UI to be user-friendly, making it simple and intuitive to onboard your models.
More accessible: This new feature makes it easy to onboard your models even if you're not a Python expert.
What can you do with it?
Upload your data: Upload your sample data, and Fiddler will automatically try to understand its structure, saving you time from entering details manually.
Review and edit: You can quickly review and edit the data structure (schema) inferred by Fiddler.
Define targets: Specify the target variable that your model is designed to predict.
Provide model details: Give your model a name and other important information.
You can access the the Model Onboarding UI on your Project pages using the new "Add Model" button as shown below:
We're excited to introduce three updates in this release that will significantly improve workflow efficiency. These updates include enhancements to alert notifications, schema management, and model onboarding workflows.
Pause Alert Notifications
Users can now pause and resume notifications for specific alert rules without interrupting their evaluation. This feature helps reduce alert fatigue and prioritize critical alerts.
Highlights:
New bell icon for toggling notification status directly from the alert rule table.
Notifications can be paused or resumed with clear visual feedback and confirmation messages.
Benefits:
Reduced alert fatigue.
Continued evaluation of paused alerts without disruption.
Model Schema Editing (Private Preview)
Say goodbye to re-onboarding models for schema updates! This feature allows users to edit numerical and categorical columns and add metadata columns directly within the platform.
Highlights:
Adjust numerical ranges and add new categories to categorical columns.
Add metadata columns for enhanced schema flexibility.
Automatic recalculation of metrics and aggregates after edits.
Benefits:
Faster schema updates with no re-onboarding required.
Keeps metrics and alerts in sync with the latest schema changes.
UI-Based Model Onboarding (Private Preview)
Simplify model onboarding with our new interactive UI. Add models without relying on Python APIs, making onboarding faster and more accessible to all team members.
Highlights:
Supports key task types: Not Set, Regression, and Binary Classification.
Automatic schema inference and validation with error detection.
Benefits:
Streamlines onboarding for non-technical users.
Reduces errors with built-in validation checks.
Private Preview
Native Integration with AWS SageMaker AI
Download Dataset Code in UI
Python Client Highlights
The latest release of Fiddler's Python client brings two powerful new convenience features to streamline your workflow:
To enhance reliability, we've implemented a configurable HTTP retry mechanism that you can fine-tune to match your network environment.
The SQL Analyze Page Discontinued
The legacy SQL Analyze page has been removed as of 24.18. The new Analyze experience within monitoring charts Root Cause Analysis now enables data table generation using Fiddler Query Language (FQL) and supports the creation of analytical charts such as confusion matrices, feature distribution charts, and more.
Feature Analytics in Root Cause Analysis (Public Preview)
The root cause analysis experience within monitoring charts now allows users to view feature distribution, feature correlation, and correlation matrix.
SQL Methods in the Python Client Discontinued
New Chart Type: Correlation Matrix (Public Preview)
The Correlation Matrix chart enables users to visualize relationships between up to eight columns in a heatmap, making it easy to spot significant patterns. By clicking on any cell representing the relationship between two features, users can open a Feature Correlation chart for that pair, offering more detailed insights into the correlation score.
Events Table in Root Cause Analysis (Public Preview)
The root cause analysis experience within monitoring charts now allows users to perform deeper investigations by viewing and downloading up to 1,000 raw events, providing valuable insights for understanding and addressing potential issues.
New Chart Type: Metric Card (Public Preview)
We’re excited to introduce the Metric Card chart type, which allows users to display up to four key numerical values in a clear and concise card format. This new visualization enhances data presentation by enabling quick insights into critical metrics, making it easier for decision-makers to spot trends or performance indicators at a glance.
New Chart Type: Feature Correlation (Public Preview)
The Feature Correlation chart, part of Feature Analytics charts, enables users to analyze and visualize the relationships between different features within their models. By offering a clear view of correlations, this tool supports more informed model diagnostics and refinement.
This release focused on system performance, stability, and security enhancements. These improvements ensure a smoother user experience and provide a more robust platform for future developments.
NEW Standalone Feature Distribution Chart (Public Preview)
Create feature distribution charts for numerical and categorical data types that can be added to dashboards.
Embedding Visualization UX Improvements
User interface and usability improvements to the UMAP embedding visualization chart.
Additional database performance improvements.
Fairness was decommissioned in v24.8, and the documentation has now been removed.
Surfacing Error Messages for Failed Jobs
Error messages for failed jobs are now visible directly on the UI job status page, simplifying the process of diagnosing and resolving issues.
User Selected Default Dashboards
Any dashboard within a project can now be assigned as the default dashboard for a model, with all insights leading directly to the assigned default dashboard.
Custom Feature Impact Feature Release Notes
Introducing Custom Feature Impact: Upload custom feature impact scores for your models, leveraging domain-specific knowledge or external data without requiring the corresponding model artifact.
Easy data upload via API endpoint with required parameters: Model UUID, Feature Names, and Impact Scores.
View updated feature impact scores in:
Model details page
Charts page
Explain page
Flexible update options: Update existing feature impact data by uploading new data for the same model and Seamless integration with existing model artifacts.
Flexible Model Deployment
The python-38 base image is no longer supported.
Client version 3.3+ is required for the updates and features mentioned in this release.
Performance Analytics (Preview) Embedded in Monitoring Charts
Visualize performance analytics charts as part of the root cause analysis flow for Binary Classification, Multiclass Classification, and Regression models, spanning from confusion matrices, precision recall charts, prediction scatterplots and more.
Client version 3.3+ is required for the updates and features mentioned in this release.
Support for applied segments in monitoring charts
Create and apply segments dynamically in monitoring charts for exploratory analysis without requiring them to be saved to the model.
User-Defined Feature Impact
The User-Defined Feature Impact enables you to upload custom feature impact for models. This feature addresses several issues reported by our customers, including model artifact size, onboarding complexity, and the need for custom feature impact.
Key highlights
New method: UploadFeatureImpact
Improved Fiddler UI to display uploaded feature impact
Enhanced access controls
Control access with precision: Manage user access to resources with Role-Based Access Control (RBAC), ensuring the right users have the right permissions.
Simplify user management: Assign roles to users and teams to streamline access control and enhance collaboration. *≠ Protect sensitive resources: Restrict access to sensitive resources, such as models and project settings, with granular permissions.
Work efficiently: Focus on your work without worrying about unauthorized access or data breaches.
Performance Analytics Charts (Public Preview)
Visualize charts to aid in analyzing model performance for Binary Classification, Multiclass Classification, and Regression models.
Leverage applied segments in Performance Analytics charts to explore problematic cohorts of data.
TBD
Performance improvements
Improved the performance of various modules / APIs.
Improved observability which can help monitor health and performance of the operations.
Client version 3.1.2+ is required for the updates and features mentioned in this release.
Support for model versions for streamlined model management
Model Versions
Efficiently manage related models by creating structured versions, facilitating tasks like retraining and comparison analyses.
Users can maintain model lineage, efficiently manage updates, flexibly modify schemas, and adjust parameters.
Airgapped Enrichments (alpha)
For privacy sensitive use cases, all data getting enriched stays within customer premises.
New Deployment Base Images
We have added new deployment base images to support model versioning.
Client Version
Client version 3.1.0+ is required for the updates and features mentioned in this release.
UMAP UI changes
SSO integration changes
New concept: Environments
Fundamental changes to product concepts
UMAP UI
Vertical scrolling instead of horizontal scrolling for data cards
"View More" option to open data cards in maximized modal
Ability to toggle between data cards in the maximized modal
SSO integration changes
Environments
Each Model now has two environments (Pre-Production and Production) used to house data in different ways.
A Model's Pre-Production environment is used to house non-time series data (Datasets).
A Model's Production environment is used to house time series data.
Product concept changes
Datasets are no longer stored at the Project level. Instead, they're stored at the Model level under the Pre-Production Environment.
The Model Details page has been updated with a new design.
Client Version
Client version 3.0+ is required for the updates and features mentioned in this release.
We are launching Client 3.x, this is revamped client 2.x as we move to more object oriented based methods. This means, any pipeline setup in client 2.x would eventually be required to upgrade to the new methods. Client 2.x will sunset approximately 6 months post this release. Please take a look at the below resources to help you understand client 3.x and also how you can upgrade your pipelines:
All IDs will be UUIDs instead of strings.
Dataset deletion is not allowed anymore.
New Glossary Feature: We've expanded our Product Concepts guide with a comprehensive . Each term now has its own dedicated page containing:
Custom LLM Enrichment: Leverage to categorize input data using your own prompts and custom categories
UI-based Model Onboarding with Draft Mode: Iteratively refine your model schema before publishing. Validate sample data, collaborate with your team, and deploy with confidence. This streamlines the model deployment process for faster time-to-value. See our new for details and best practices.
Fast Faithfulness Trust Model Enhancements: Improved classification accuracy overall by 23% and reduced the Q&A benchmark error rate by 36%. For technical details, see our .
New UI Onboarding Guide: Published detailed for the new UI-based model onboarding feature, including step-by-step instructions and best practices.
We have added Token Count as a new addition to Fiddler's out-of-the-box . Token count visibility is a key factor for monitoring and optimizing LLM applications. This enrichment is particularly useful for cost analysis, as it tracks API usage and helps teams understand the financial implications of their LLM usage. It also aids in identifying performance issues related to token limits and supports system health monitoring by detecting unusual patterns or truncated responses. Teams can use token metrics to optimize prompts for efficiency and quality while understanding usage patterns across their application. Combined with existing quality metrics, token counts offer a more complete view of LLM system performance and help teams make data-driven decisions about their prompt engineering and resource allocation.
Python client version is updated to and includes new support for updating additional parameters of including warning_threshold, critical_threshold, and evaluation_delay.
Fiddler AI has introduced Fiddler Guardrails, a new feature that extends the Fiddler Trust Service and is designed to enhance the safety and security of Large Language Model (LLM) applications. This tool proactively detects and mitigates risks such as hallucinations and prompt injection attacks, ensuring more reliable and trustworthy AI operations. Organizations can confidently deploy LLM applications with improved oversight and protection by integrating Fiddler Guardrails. You can see the full announcement for a comprehensive overview of this feature.
This release updates the following Guardrails REST API endpoints. The provides detailed usage information.
Onboard models for different tasks: Currently, it supports three : Not Set, Regression, and Binary/Multiclass Classification.
This feature is currently in public preview which you can learn more about . We appreciate your feedback as we work to enhance the UI Model Onboarding experience.
The Model Schema Editing and UI-Based Model Onboarding features are available for private preview. For more details on how to participate, please refer to the private preview .
Fiddler is now natively supported within the newly launched Amazon SageMaker partner AI ecosystem. This integration enables enterprises to validate, monitor, analyze, and improve their ML models in production, all within their existing private and secure Amazon SageMaker AI environment. Read the official announcement . Note Fiddler Python client version is required for this feature.
Now you can download your baseline and non-production datasets faster than ever with just a click! Building on the popular feature we introduced for production data in the Root Cause Analysis Events table, we've added ready-to-use Python code snippets right in the interface. Simply copy and paste these snippets to jumpstart your data analysis in your notebooks.
Our new class function simplifies project creation in notebooks. This feature prevents name conflict errors during project creation when your notebook runs multiple times, saving you time and reducing the need for additional exception handling.
We've also added , a simpler way to remove columns during model onboarding. This function replaces the multi-step process previously required, making model configuration faster and more intuitive.
For more details, please refer to the .
Client version is required for the updates and features mentioned in this release.
From Client 3.6 and onwards, get_slice and download_slice are discontinued. In their stead, use the new method to download production and non-production data from your Fiddler models. If you have any questions or need any assistance migrating scripts using the deprecated methods, please contact your Fiddler customer success manager.
Use of or support for Python 3.8 is discontinued by Fiddler. Note Python 3.8 has been designated as of October 7, 2024.
See: for more information.
See .
Fiddler now integrates with Azure AD SSO, allowing you to leverage existing user roles for access control within Fiddler. This eliminates the need for manual user creation and simplifies user management within your organization. See for details
For API level changes and updates please check
