Fiddler AI is an AI observability platform that enables organizations to monitor, explain, and improve their machine learning models throughout their lifecycle. It provides tools for detecting model drift, assessing performance, and ensuring data integrity. With built-in explainable AI techniques, Fiddler helps teams understand the "why" behind model predictions, fostering trust and compliance. The platform integrates seamlessly with existing MLOps and cloud infrastructures, offering both cloud-hosted and on-premises deployment options to suit various enterprise needs.
Key Features
Real-time model performance monitoring and drift detection
Explainable AI with Shapley values and proprietary methods
Root cause analysis and anomaly detection
Bias and fairness assessment tools
Integration with cloud platforms like AWS, Google Cloud, and Databricks
Role-based access control and single sign-on (SSO) support
Customizable dashboards and alerting systems
How Does It Work?
Fiddler AI collects data from deployed machine learning models to monitor their performance continuously. It analyzes inputs, outputs, and model behavior to detect anomalies such as data drift, performance degradation, or bias. The platform provides explainability features, offering insights into the factors influencing model predictions. Users can set up alerts for specific metrics, enabling proactive management of model health. Fiddler's integration capabilities allow it to fit seamlessly into existing MLOps pipelines, providing a centralized view of model performance and compliance.
Step-by-Step Overview:
- Sign Up: Create an account on Fiddler AI's platform.
- Integrate Models: Connect your machine learning models to the platform.
- Configure Monitoring: Set up performance metrics, drift detection, and alerting parameters.
- Enable Explainability: Utilize built-in tools to understand model predictions.
- Monitor and Analyze: Continuously track model health and performance.
- Take Action: Use insights to optimize models and address issues promptly.
Ideal Users for Fiddler AI
Data Scientists
MLOps Engineers
AI Product Managers
Compliance Officers
Business Analysts
Software Engineers
Quality Assurance Teams
Research Scientists
IT Administrators
Risk Management Teams
CTOs
Regulatory Affairs Specialists
Customer Success Managers
Operations Managers
Data Engineers
Marketing Analysts
Financial Analysts
Healthcare Analysts
Case Studies & Examples
Case Study 1: Financial Services Compliance
- Challenge: A leading financial institution utilized Fiddler AI to monitor its credit scoring models.
- Solution: Detected model drift and bias, ensuring compliance with regulatory standards.
- Result: Enhanced transparency and trust, allowing auditors to understand decision-making processes.
Case Study 2: Healthcare Predictive Analytics
- Challenge: A healthcare provider implemented Fiddler AI to oversee predictive models for patient readmission.
- Solution: The platform identified data integrity issues and performance degradation.
- Result: Improved accuracy by retraining models and enhancing decision-making for better patient care.
How to Deploy and Integrate
- Sign Up: Register for Fiddler AI's platform.
- Integrate Models: Connect your machine learning models using available APIs or SDKs.
- Set Up Monitoring: Define performance metrics, drift detection thresholds, and alerting rules.
- Enable Explainability: Configure explainable AI features for model transparency.
- Deploy: Implement the monitoring system in your production environment.
- Review and Optimize: Regularly assess model performance and make necessary adjustments.
Pricing & Plans
| Plan Name |
Features |
Price |
| Lite |
Basic monitoring, limited integrations, and community support |
Contact Sales |
| Business |
Advanced analytics, explainability tools, and role-based access |
Contact Sales |
| Premium |
Full feature set, custom integrations, dedicated support |
Contact Sales |
💡 Pricing is customized based on enterprise needs and deployment scale.
Pros and Cons
Comprehensive monitoring and explainability tools
Seamless integration with major cloud platforms
Supports both cloud and on-premises deployments
Facilitates compliance with regulatory standards
Provides actionable insights for model optimization
Pricing may be prohibitive for small enterprises
Initial setup may require technical expertise
Some advanced features may have a learning curve
