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Related Questions
- What are some strategies for domain adaptation to improve model performance when there is a significant difference between the training and deployment environments?
- How can data augmentation techniques be used to reduce the impact of ontological mismatch between training and deployment environments?
- What are some techniques for fine-tuning pre-trained models to adapt to different environments and reduce ontological mismatch?
- What are the common causes of ontological mismatch between training and deployment environments, and how can developers identify them?
- Can you explain the concept of semantic drift and how it relates to ontological mismatch between training and deployment environments?
- How can developers use transfer learning to mitigate the effects of ontological mismatch between training and deployment environments?
- What are some best practices for developing and testing models in a way that minimizes the risk of ontological mismatch between training and deployment environments?
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