Enterprises heavily rely on accuracy and explainability of NLP model predictions and insights to inform NLG (natural language generation) strategies for creating equitable customer experiences at scale for deeper engagement, brand satisfaction, and retention to increase customer lifetime value (CLV).
In this guide, learn about Arthur's monitoring support for NLP models, including data drift detection, token-level explainability, as well as model bias detection. Proactively monitor for and correct data drift in your NLP models using Arthur, maintaining their accuracy and integrity over time.
Learn about Arthur's accuracy, explainability, and bias mitigation features for NLP models.
Trusted by Fortune 100 Leaders
Fortune 100 leaders across financial services, healthcare, retail, and tech trust Arthur to monitor and improve their ML models to drive business impact.
“Thanks to Arthur, we know that our preventative care models are fair, and that we can catch any potential issues before they impact our members…and the Arthur platform allows us to detect and fix data drift before it becomes a real problem.” — Chief Analytics Officer, Humana
”The biggest challenge is looking at this from a reputational risk perspective. The last thing we want is to be on the front page of the news with a bias issue.” — Global Tech VP
“Arthur is 6-9 months ahead of the competition and there was a clear preference for their UX among our data scientists.”— Head of Global Artificial Intelligence
Monitor, measure, and improve ML models with Arthur
Arthur helps data scientists, product owners, and business leaders accelerate model operations at scale. We work with enterprise teams to measure and optimize model performance in production for:
Accuracy & Data Drift
Explainability & Transparency
Fairness & Bias Mitigation
Track model performance to detect and react to data drift, improving model accuracy for better business outcomes.
Build trust, ensure compliance, and drive more actionable ML outcomes with Arthur’s explainability and transparency APIs.
Proactively monitor for bias, track model outcomes against custom bias metrics, and improve the fairness of your models.