Train the model in Autotune mode

In Autotune mode, the model automatically adjusts its hyperparameters (such as learning rate, batch size, or regularization techniques) during training to optimize its performance.

This mode leverages algorithms that dynamically find the best combination of hyperparameters without requiring manual intervention. The main goal is to find the optimal configuration for the model to improve accuracy, minimize error rates, or balance other performance metrics.

Autotune mode simplifies model training, making it more efficient and reducing the need for experimentation.