Fiddler is a commercial platform focused on AI model monitoring, explainability, and performance analysis in production environments. It’s built for data scientists, ML engineers, MLOps teams, and business stakeholders who need visibility into model behavior, fairness, drift, and performance degradation across the full lifecycle of an AI system.

What sets Fiddler apart is its strong emphasis on explainability, enabling users to understand not just what went wrong with a model, but why. The platform supports real-time and batch model monitoring, integrates easily into existing ML pipelines, and provides actionable diagnostics to ensure that AI systems are trustworthy and compliant.

Key benefits:

• Combines monitoring, drift detection, and explainability in one solution

• Offers model-agnostic insights with detailed root cause analysis

• Helps organizations meet governance, fairness, and regulatory requirements

What are the main features of Fiddler?

Real-time model monitoring and performance tracking

Fiddler continuously monitors models in production to detect issues as they emerge:

• Tracks prediction distributions, confidence scores, latency, and output stability

• Monitors both classification and regression models

• Supports granular analysis by feature, segment, or time period

• Integrates with CI/CD systems and real-time data streams

Drift detection across data and predictions

Detects shifts in input data and output distributions over time:

• Monitors data drift (input feature changes) and prediction drift

• Uses statistical tests to measure distributional changes

• Highlights affected features and quantifies drift magnitude

• Supports alerting and automated drift reporting

Explainability and root cause analysis

Fiddler’s core strength is its AI explainability engine:

• Provides feature importance, SHAP values, and global vs. local explanations

• Helps trace why predictions changed and what influenced them

• Enables counterfactual analysis to understand “what if” scenarios

• Useful for debugging, model validation, and user-facing transparency

Bias and fairness detection

Ensures AI models behave ethically across different user groups:

• Evaluates fairness metrics like disparate impact, equal opportunity, demographic parity

• Tracks performance across sensitive attributes (e.g., race, gender, age)

• Visualizes where models may underperform or treat groups unfairly

• Supports compliance with responsible AI standards

Collaboration and governance features

Fiddler is built for use across technical and business teams:

• Provides audit trails, version control, and role-based access

• Allows stakeholders to review, compare, and approve models

• Centralizes monitoring, explanations, and decisions in a single platform

• Helps enforce AI governance frameworks and reporting requirements

Why choose Fiddler?

• Integrated monitoring and explainability: Understand not just what failed, but why

• Model-agnostic and deployment-flexible: Works across frameworks and infrastructures

• Supports ethical and responsible AI: Tools for fairness, compliance, and transparency

• Designed for collaboration: Enables cross-functional decision-making

• Production-grade reliability: Scalable and secure for real-world AI operations