Want to decrease time-to-production and increase the number of models you release? How about resolving ML model issues with speed so you can achieve faster time-to-market?
The centralized dashboard delivers deep insights into model behavior and uncovers data pipeline issues to save debugging time. The Fiddler AI Observability platform operates at enterprise scale so you can go to market faster by monitoring and validating models during pre-deployment phases and releasing them in production.
Adoption of an enterprise-scale solution saves you the ongoing costs associated with building and managing an in-house ML monitoring solution. More importantly, it saves engineering and data scientists’ time so they can focus on what they do best: building ML models.
Reducing errors saves money, and delivering high-performance models to customers increases satisfaction and referrals.
If you have process silos and disparate monitoring solutions, you are at risk of operational inefficiencies, not to mention losing out on the benefits associated with collaboration.
Monitor all training and deployed models in one place for streamlined detection of data changes. The Fiddler intelligent platform empowers teams to come together, discover, discuss, and fix issues.
Track your model’s performance and accuracy with out-of-the-box metrics, including binary classification, multi-class classification, regression, and ranking models
Easily monitor data drift, uncover data integrity issues, and compare data distributions between baseline and production datasets to boost model performance
Use popular drift metrics like Jensen-Shannon Divergence (JSD) and Population Stability Index (PSI) to uncover any data drift and help calculate which drifting features are impacting your model’s accuracy
Increase prediction accuracy by monitoring complex and unstructured data, such as natural language processing and computer vision, in your models
Detect changes in low-frequency predictions due to class imbalance in each stage of your ML workflow
Uncover data integrity issues in your data pipeline, including missing feature values, data type mismatches, or range violations
Update ground truth labels in a delayed, asynchronous fashion
Configure and receive real-time alerts to identify and troubleshoot high-priority issues caused by performance, data drift, data integrity, and traffic metrics