I don't explicitly cover Ensemble Learning and Deep Learning in this book with great detail, but this could be a good "philosophical" step in the journey to both of those topics. In the Google Cloud section, I have an example of using TPUS (I think this may be the first book with an example since I was given Alpha access to them), and some Deep Learning is done via Tensorflow on a TPU. This is a very basic example though.
Ensemble Learning is only talked about in the context of the Netflix prize where I mention that the winning approach, an ensemble learning method, wasn't implemented because of the complexities of putting it into production. I think there is a lesson here, and that is that complex ML techniques may ultimately not make it into production if the operationalization is not accounted for. This is where Managed Machine Learning systems: Sagemaker, etc, play a role. They abstract away ML operational complexity.