The enormous size of the input space and complexity inherent in human languages makes it difficult and time-consuming to validate model pipelines and rigorously test model behavior. This is true of NLP systems developed in-house, and perhaps even more true of systems provided by third parties. Robust Intelligence offers a powerful, largely-automated and continually improving framework for ensuring the integrity of your NLP models.
Ensure that your models are invariant to benign data transformations, robust to noise and attacks, and generalize across subdomains. Automatically catch vulnerabilities in your models before they go into production.
Secure your production NLP models to ensure they remain performant in the face of problematic data points. Our AI Firewall automates anomaly detection, intercepting bad data before it can reach your model to eliminate failure.
Overcome notoriously difficult NLP monitoring challenges by also tracking semantically relevant features of text data. Continuously monitor models in production to identify issues, understand when it’s time to retrain a model, and automate root cause analysis of model failure.
Without proper system design, even state-of-the-art transformer models can be fooled by imperceptible text perturbations. We quantify your models’ vulnerability to adversarial attacks and improve your service’s robustness.
Evaluate any text classification or named entity recognition (NER) model on any dataset. We configure tests based on your setup to serve insights relevant to your use case.
Language isn’t created in isolation. Use metadata attributes to test model performance in context and catch when your model is underperforming.
We make it simple to include state-of-the-art language models in your ML Integrity workflow with our native Hugging Face integration, enabling you to confidently accelerate NLP adoption using open source resources.