The platform guarantees quality through a dual mechanism:
- Model fairness: Provided"Reduce bias" optionThe training parameters are automatically adjusted to reduce the error rate; support for cross validation of multimodal data (e.g., correction of image recognition results with text annotations)
- data security: Adoptionend-to-end encryptionand GDPR-compliant storage, organizations can set upfine-grained permissions(e.g., restricting customer service team access to voice module only)
- transparent monitoring: Dashboard displays metrics such as memory usage in real-time, and audit logs are triggered for abnormal operations.
For sensitive data such as healthcare, private deployment solutions are also supported.
This answer comes from the articleRadal: a low-code platform for rapid fine-tuning and optimization of AI modelsThe
May not be reproduced without permission:AI productivity tools " How does Radal ensure fairness and data security in AI models?
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