AI is eating the world, but the ML models that power intelligent applications are prone to failure. Data science teams are always on alert, reacting to changes observed in dashboards and writing ad hoc tests. The need to constantly firefight model errors introduces risk and makes it difficult to apply ML at scale.
AI Stress Testing
Before models are put into the wild, we proactively run hundreds of tests to automatically identify potential issues and suggest improvements for model production readiness.
Once models are ready for deployment, we automatically configure and deploy a wrapper around your model to prevent bad data from entering your system in real time.
increase in data science team productivity through automated testing and monitoring of ML models
more models deployed into production per year
decrease in the mean time to resolution of production machine learning issues
reduction in cost and time for model compliance verification