Model drift mitigation for your models
Alert when models deviate from the intended outcomes. Be proactive and catch drifts before they become problems.
Track and visualise model insights
Easily integrate with your apps and scale your analysis across hundreds of billions of nodes and relationships.
Make your models fair and unbiased
Manage and monitor fairness. Scan your deployment for potential biases.
_ Trusted AI
Operationalise AI with trust and confidence
Build trust in production AI. Rapidly bring your AI models to production. Ensure interpretability and explainability of AI models. Simplify the process of model evaluation while increasing model transparency and traceability.
_ AI observability
Speed time to AI results
Systematically monitor and manage models to optimize business outcomes. Continually evaluate and improve model performance. Fine-tune model development efforts based on continuous evaluation.
_ Risk
Mitigate risk and cost of model governance
Keep your AI models explainable and transparent. Manage regulatory, compliance, risk and other requirements. Minimise overhead of manual inspection and costly errors. Mitigate risk of unintended bias.
_ Case study: INCISIVE
An AI-powered cancer image repository for diagnosis, prediction and follow-up
INCISIVE aims to create a pan-European platform of annotated cancer images for doctors and researchers in the field. The images will be annotated for cancer detection using state-of-the-art AI models and the most recent ethical practices.
Squaredev’s main role in the project is to provide an XAI (explainable AI) service so that doctors and researchers can understand how the models came to produce the outcomes they are reading.