Deploying machine learning responsibly means validating models during development, monitoring models once they are in production, and checking that incoming data adhere to expectations before using the model. Robust Intelligence provides solutions for these different use cases, and more.
Production-ready models need to be rigorously tested to ensure they are not overly sensitive, that they perform well across subsets of data, and that they effectively handle distribution shifts and abnormal inputs. Automatically test these principles in your CI/CD workflow with AI Stress Testing and ensure that models conform to your standard definition of production readiness.
Machine learning models are notorious for making confident but incorrect predictions on data points with outliers, missing values, unseen categories, and other abnormalities. Protect your model from unexpected inputs by using our AI Firewall to flag, block, and impute individual inputs that your model isn’t well-equipped to handle.
Asserting that models are compliant with regulations before releasing them to the world is an important, but time-consuming, effort. It’s made effortless with Robust Intelligence. Select from our database of compliance-focused tests, synthesize the results into a report, and maintain an audit log of your production models.
With data streams constantly shifting and evolving, a model trained last week might not be performing well today. Once your model is in production, use our AI Continuous Testing to monitor and alert on distribution drifts and abnormal inputs which may be causing your model to fail. Know when it’s time to retrain your model and automate the root cause analysis of model failures.
Many machine learning models can be fooled by malicious actors into making incorrect predictions with virtually unnoticeable changes to the input, known as adversarial attacks. Harden your model against such vulnerabilities by using our suite of black-box adversarial attack tests to augment your data, inform your training algorithms, and identify your most robust candidate models.