Hi,
Adding some more details on top of @lgusm reply.
In TensorFlow/Keras, there are two ways to load and use a model:

The SavedModel API : tf.saved_model.load('./my_model').
The Keras API: tf.keras.models.load_model('./my_model').

The Keras API calls the SavedModel API and adds some extra utility functions. If you can the choose, I would recommend using the Keras API.
Once a model is loaded you can use if as follow:
# Generate some predictions. Works with all APIs.
# dataset can be a tensor, array, structure of tensor or structure of arrays.
# All tensors need to be of rank>=2.
predictions = model(dataset)

# Generate some predictions. Works only with the Keras APIs.
# dataset can be anything as before. Also support dataset being a tf.dataset.
# There are not constrains on the tensor ranks.
predictions = model.predict(dataset)

# Evaluate the model.
metrics = model.evaluate(dataset)

Cheers,
M.