AI Failure is when you train an ML model and it behaves poorly in production because of issues like novel corner case inputs, upstream ETL changes, and distributional drift. Data science teams constantly face these issues and more, spending time root causing and firefighting.
Data science teams may optimize for a single performance metric like accuracy, but this is inadequate to prevent AI Failure. Combatting AI Failure takes time and energy. Robust Intelligence helps to prevent AI Failure so that you can focus on what truly matters.
Broken Data Pipelines
Train a candidate model, and automatically discover its individual weaknesses with AI Stress Testing. Go beyond simply optimizing for model performance.
Improve the model with automatic suggestions. Compare with other candidate models. Establish and enforce standards across your organization.
Confidently deploy the best model into production with AI Firewall with one line of code.
Observe your model in production and automate the discovery and remediation of any issues that occur post-deployment. Automatically flag, block, or impute erroneous data in real-time.