  Directed evolution of proteins has been the most effective method for protein
engineering. However, a new paradigm is emerging, fusing the library generation
and screening approaches of traditional directed evolution with computation
through the training of machine learning models on protein sequence fitness
data. This chapter highlights successful applications of machine learning to
protein engineering and directed evolution, organized by the improvements that
have been made with respect to each step of the directed evolution cycle.
Additionally, we provide an outlook for the future based on the current
direction of the field, namely in the development of calibrated models and in
incorporating other modalities, such as protein structure.
