Here are some callbacks that I have found to be very useful when training machine learning models using python and tensorflow:

Number one: Early stopping

Keras early stopping (https://keras.io/api/callbacks/early_stopping/) has to be my favorite callback. With it you can define when the model should stop training if it is not improving. An example for usage is:

earlystopping = tf.keras.callbacks.EarlyStopping(
    monitor="val_loss",
    min_delta=0.001,
    patience=5,
    verbose=1,
    restore_best_weights=True,
)

This will stop the model's training once it does not improve at least 0.001 in loss for 5 epochs. It will then restore the model's weights to the weights on the best epoch. Just like any callback make sure to include it during training like
model.fit(some_data_X, some_data_y, epochs=some_number, callbacks=[earlystopping, some_other_callback])

Number two: Learning rate scheduler

Keras learning rate scheduler (https://keras.io/api/callbacks/learning_rate_scheduler/) can be very useful if you are having problems with your learning rate. With it you can reduce or increase learning rate during training based on a number of conditions. An example:

def scheduler(epoch, lr):
       return lr * tf.math.exp(-0.5)

 learningratecallback = tf.keras.callbacks.LearningRateScheduler(scheduler)

The scheduler function is where you can define your logic for how the learning rate should decrease or increase. learningratecallback just wraps your function in a tf.keras.callbacks.LearningRateScheduler(). Don't forget to include it in model.fit()!

Last but not least, number three: Custom callbacks

Custom callbacks (https://keras.io/guides/writing_your_own_callbacks/) are great if you need to do something during training that is not built in to keras + tensorflow. I won't go in depth as there is a lot you can do. Basically you have to define a class that inherits from keras.callbacks.Callback. There are many different functions that you can define that will be called at different times during the training (or testing and prediction) cycle. A simple example would be:

class Catsarecoolcallback(keras.callbacks.Callback):
    def on_epoch_end(self, logs=None):
        print('cats are cool!`)
callback = Catsarecoolcallback()

This (as you can probably tell) prints out cats are cool! every time an epoch ends.

Hope you learned something while reading this!