The Fiddler AI Observability platform delivers the best interpretability methods available by combining top explainable AI principles, including Shapley Values and Integrated Gradients, with proprietary explainable AI methods. Obtain fast model explanations and understand your ML model predictions quickly with Fiddler Shap, an explainable method born from our award-winning AI research.
Hear from our Data Science team about different Explainable AI concepts:
To ensure continuous transparency, Fiddler automates documentation of explainable AI projects and delivers prediction explanations for future model governance and review requirements. You’ll always know what’s in your training data, models, or production inferences.
You can deploy AI governance and model risk management processes effectively with Fiddler.
Not only do you save money from potential fines and penalties, but the likelihood of any negative publicity is reduced when you can detect and resolve issues or deep-rooted model bias before it is exposed.
It’s important to understand model performance and behavior before putting anything into production, which requires complete context and visibility into model behaviors — from training to production.
When areas of low performance or potential issues are rooted out before they are seen, the customer experience is improved, leading to higher Net Promoter Scores (NPS) and stronger customer recommendations.
Zoom in to the details
Increase your model’s transparency and interpretability using SHAP values, including our award-winning Fiddler SHAP metric.
Run deep learning models, including NLP and CV models, faster and comprehend how data features contribute to data skew and model predictions.
Gain a better understanding of your model’s predictions by changing any value and studying the impact on scenario outcomes.
Understand how each feature you select contributes to the model’s predictions (global) and uncover the root cause of an individual issue (local).
Improve the interpretability of your models before they go into production by using automatically generated surrogate models.
Create customized explanations specific to your use case via APIs.