Efemarai Continuum™ is a platform for continuous testing and improvement of machine learning models that can automatically discover failure edge cases by generating new and meaningful data samples.
Ensure your model operates consistently throughout its entire operational domain by describing what your data looks like and how it can vary.
Discover unseen edge cases by generating new and meaningful data samples falling within the expected operational domain, but causing model failures.
Highlight areas of sub-par performance and catch regressions before the model is deployed in production. Make it part of your integration flow and always know how to iterate next.
Expand your training set with data that improves your model. Use synthetic failure data samples or automatically flag new incoming data that is likely to cause performance issues.
Efemarai Continuum™ supports all popular machine learning frameworks and development platforms such that it can be easily integrated into your workflow.
Curious how Efemarai Continuum™ can help you with your use case?
Let’s make ML models more robust by taking inspiration from modern software testing! A new way to confidently improve your models.
Deploying Machine Learning models to the real world is prone to uncover domain coverage issues. Property based testing can help.