What are the best practices for fine-tuning AI models?

What are the best practices for fine-tuning AI models?

Fine-tuning AI models involves several best practices, such as using a well-curated dataset, adjusting hyperparameters, and monitoring performance.

Can you elaborate on the importance of dataset quality?

Absolutely! High-quality datasets ensure that the model learns relevant patterns and reduces the risk of overfitting.

What hyperparameters should I focus on during fine-tuning?

Key hyperparameters include learning rate, batch size, and the number of training epochs. Tuning these can significantly impact performance.

How do I know if my model is overfitting?

You can check for overfitting by comparing training and validation loss. If the training loss decreases while validation loss increases, overfitting may be occurring.

Any tools you recommend for monitoring model performance?

Yes, tools like TensorBoard and Weights & Biases are great for visualizing training metrics and monitoring model performance.