Yes that is essentially what I have been doing. I made some tweaks to accommodate for Google Colab Pro dying on me as well. To help out @Daniel_Kuhn here are some snippets:
# Mount the Google Drive of 'XXX@gmail.com' account so we can access 'TRAINING_DATA.zip'
from google.colab import drive
drive.mount('/content/drive')

# Extract the training data from Google Drive
!unzip /content/drive/MyDrive/TRAINING_DATA.zip

# Path on Google Drive where we will save the model
SAVED_MODEL_PATH = f"./drive/MyDrive/my_saved_model_{model_name}"

# 5 epochs at a time
for i in range(0, 5):
  hist = model.fit(
      train_ds,
      epochs=1, steps_per_epoch=steps_per_epoch,
      validation_data=val_ds,
      validation_steps=validation_steps).history
  # Save the model to Drive after every epoch, so that it can be reloaded to continue training, if the notebook dies    
  model.save(SAVED_MODEL_PATH)


# If the Google Colab dies, remount the Google drive as before, reload the saved model and continue training
model = tf.keras.models.load_model(SAVED_MODEL_PATH)

Rest of the code is similar to what @lgusm pointed us to: Retraining an Image Classifier | TensorFlow Hub 1