Minimize machine learning risks and maintain model compliance at scale
Maintain regulatory compliance across ML initiatives
Be in control of your ML models and stay ahead of model, data or regulatory changes at your fingertips.
Accelerate AI time-to-value and scale
Increase ML operational efficiencies
Build trusted AI solutions
Minimize risks impacting your brand and reputation
Improve predictions with context for business alignment
Enable model interpretability for all stakeholders
Companies trust Fiddler to monitor models for regulatory compliance
Avoid risks that negatively impact your company’s brand and revenue. The Fiddler AI Observability platform supports teams in building out a robust model governance and model risk management framework to meet regulatory compliance, build trust into your AI solutions, and adopt responsible AI.
Model governance
Adopt effective model governance to minimize risks to your company’s reputation.
Audit all models in your inventory by tracking model performance and data drift at scale. Empower stakeholders with fine-grained control and visibility into model accuracy, and understand the root cause of model degradation. Perform segmentation analysis on anomalies across models to quickly mitigate potential risks and improve operational efficiencies.
Regulatory compliance
Ensure all your models stay compliant through model monitoring.
Evaluate model performance and how shifts in structured (tabular) and unstructured (natural language processing and computer vision) data attribute to model degradation or model bias. Be alerted to data integrity issues in your pipeline, from missing values to range or data type violations. Quickly respond to regulatory requirements with insight into model changes.
Model risk management
Build a robust model risk management framework with greater model transparency and explainable AI.
Enable regulators and stakeholders across the business to facilitate compliance throughout the MLOps lifecycle and share MRM and risk reports for periodic reviews. Avoid financial risks, fines, or breaches by understanding how changes in model performance or data can alter model predictions.
Head of Data Science, Conjura
