Government agencies are increasingly adopting AI to support a variety of use cases ranging from low-risk back office applications to high-stakes decision making. Mission critical applications that use AI to make decisions about unstructured data, like text and image, require oversight to ensure that they continue to perform as designed, and be understood for decision making.
The Fiddler AI Observability platform helps establish standardized ML and LLMOps practices. The Fiddler AI Observability platform monitors ML, LLM, and generative AI models in pre-production and production, and helps AI teams ship more models and apps into production. Fiddler supports government and federal agencies' AI applications with a responsible diagnostic layer to support personas ranging from model developer, updating models and root causing problems, to a human operator whose AI assisted decision making can be enhanced by understanding model confidence and reasoning.
Fiddler enables model developers to measure subtle changes in streams of image data that their model is sensitive to. Additionally, it allows them to identify and isolate common failure cases with semantic clustering and model explainability. Fiddler provides model operators, "the human in the loop", with visual explanations of model decisions that fortifies trust in their tools and allows AI to enhance their expert decision making, rather than overriding it.
Fiddler’s Patented Algorithm for Monitoring Unstructured Data
Fiddler’s patented clustering-based algorithm for Vector Monitoring goes beyond traditional methods to monitor unstructured data like like natural language processing (NLP), computer vision (CV), and multi-modal AI, by creating a nuanced empirical density estimate in the model's embedding space, capturing high-level semantic information that is often missed by other tools. Fiddler detects high density regions in the embedding space of the baseline data, and tracks how the relative density of such regions changes over time.
Government Use Cases
Image Explainability for Autonomous Vehicles
Fiddler explains predictions in ML models powering autonomous vehicles, such as aerial drones and unmanned underwater vehicles, to ensure they are high-performing and accurate during missions across ground, air, surface and subsurface environments.
Identify anomalies to anticipate potential threats and navigation
Track differences in operational domain of imagery or sensor data compared to training-time
Enhance human effectiveness in high-stakes post-mission analysis decision-making
CV Monitoring for Intelligence and Surveillance
Fiddler monitors image models to track and get real-time alerts on anomalies and data drift.
Monitor changes in traffic, signals, and communication patterns
Incorporate human-in-the-loop process for assessment and decision making
Model Monitoring Sensor Data
Fiddler monitors unstructured models to detect sensor data drift and identify outliers.
Improve target precision with high performance model monitoring across different battlefield environments