Explainable AI Monitoring = High-Performance

AI has unique operational challenges that need continuous monitoring: from identifying drifting data and performance dips to pinpointing outliers, Fiddler keeps your AI on track.

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Unique operational challenges in ML models

Lack of in-depth visibility into deployed systems results in suboptimal solutions and potentially harmful outcomes

Unique characteristics of ML models demand robust monitoring to manage issues like model decay

Inherent opaqueness of machine learning models makes them harder to understand

Fast Issue Resolution

Mitigate challenges with solving production ML issues by unlocking blackbox AI models using explainability

Deep dive into production issues using explainability and model analytics with global, local, and regional comparisons

Save time debugging issues with deeper insights and reasoning behind model behavior

Streamlined detection of data changes

Use data drift detection to maintain quality without direct indicators of model performance

Dive into the causes behind data drift for faster resolution

Use the specifics behind problem drivers to better inform model retraining

Stay on top of anomalies

Detect outliers easily and understand which ones are critical, threat or otherwise.

Get a bird’s eye view of all your outliers or easily pinpoint those caused by a specific model input

Probe into each outlier using one-click explanations for fast problem assessment

Take immediate action with alerts.

Set it and forget it. Get alerted for a wide range of issues and manage your model’s unique needs.

Solve issues quickly by zooming into alerts, troubleshooting in context,  and identifying the root cause

Use a powerful alert management dashboard to effectively manage all your alerts

Build trustworthy and explainable AI solutions with Fiddler.

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