Production models will inevitably receive “bad” data that they weren’t trained to support. Our AI Firewall can be configured to intelligently flag, block, or sanitize data points before they ever reach your model. This helps eliminate failure and boost model performance.
Manual validation of every new dataset and model can be incomplete and take a painstaking amount of effort. Our AI Firewall automates the task of testing data quality. As each model is deployed, we auto-configure a custom set of requirements, ensuring trust and reducing time spent writing custom validation logic.
Rl uses the learnings of stress testing to automatically configure the AI Firewall, but there is customizability in the treatment of aberrant data. Choose to change the default configuration to flag, block, or impute identified bad data points.
All models eventually need to be retrained. The AI Firewall runs a comprehensive suite of validation logic on incoming production data in real time. It's able to surface key issues and alert on especially problematic data points so that the user is able to take proactive remediative actions. This end result is reduced model downtime.
Our AI Firewall is automatically configured from the weaknesses discovered during AI Stress Testing.
Protect your model against bad data points in near real-time, introducing minimal latency.
Deploy in a single line of code with our easy-to-use Python library interface.
Connect into your production AI framework, with hooks into model inference as well as downstream monitoring applications.