The Radal platform provides specialized tools and methods to optimize model performance:
- Data quality analysis:After uploading the dataset, the system automatically detects the format and provides optimization suggestions
- Parameter Adjustment:
- Intuitive adjustment of the model structure (e.g., adding an attention mechanism or modifying the number of layers) in the training interface
- Reduced bias option:Optimization features specifically designed to reduce model error rates
- Performance Monitoring:Dashboard showing key metrics such as accuracy, loss values, etc. in real time
- Multiple rounds of testing:Support for exporting ONNX or TensorFlow formats for validation
For industry-specific tasks, such as medical diagnosis or financial risk assessment, the platform also provides domain-specific pre-training templates that can be directly applied and fine-tuned based on business data.
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 " What are practical ways to optimize AI model accuracy and reduce bias?
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