Use cases

Get visibility. Increase productivity. Build optimally.

Even with the endless benefits of AI, there is potential for unintended, biased results. Instead of realizing AI and ML performance issues after the damage has been done, monitor ahead of time. With Explainable Monitoring, get visibility into the “blackbox” to see exactly how AI and ML models operate and get to the root cause of issues fast.

Trust & Visibility
Monitor everything and stay in the know to
ensure biases, data issues, and model decays are found and solved fast.

Deploy Successful AI
Real-time monitoring provides visibility, transparency, and insights into the Blackbox to see how models operate and ensure success.

Save Time & Money
Get fast issue identification and resolution with explanations, while ensuring compliance with industry regulations to minimize fines.

Challenges in ML monitoring

Complex ML models are black-boxes and tend to exhibit inexplicable characteristics.

ML models have unique operational needs like model decay and data integrity that demand more robust monitoring.

Fairness issues rise to the top and generate negative outcomes with no effective way to track and resolve them.

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Lending prediction risk – Explanation chart

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Want a demo of how Fiddler can enable you to build better AI models? Understand what ML monitoring involves, the 5 key operational challenges, and Fiddler’s approach with Explainable Monitoring.

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Build trustworthy and explainable AI solutions with Fiddler.

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