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Related Questions
- How do computational resource constraints impact the performance of deep learning models in real-time applications?
- What are the trade-offs between model complexity, computation time, and accuracy when deploying pre-trained models?
- How does the choice of hardware platform (e.g., CPU, GPU, TPU) affect the deployment of custom-trained models?
- What are some strategies for optimizing model architectures and hyperparameters for efficient deployment on resource-constrained devices?
- Can you discuss the implications of model compression and knowledge distillation for reducing computational overhead in real-world applications?
- How do real-world applications handle issues such as model drift and concept drift, which can impact the performance of deployed models?
- What are some best practices for monitoring and maintaining the performance of deployed models in real-world applications?
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