In most cases, you'll want to retrieve your model after training on Google Cloud. For this, it's crucial to redirect saving and loading to Cloud Storage while training remotely. We can direct TensorFlow Cloud to our Cloud Storage bucket for a variety of tasks. The storage bucket can be used to save and load large training datasets, store callback logs or model weights, and save trained model files. To begin, let's configure fit() to save the model to a Cloud Storage, and set up TensorBoard monitoring to track training progress.