Before revision:  They accelerate protein engineering by learning from information contained in all measured variants and using it to select variants that are likely to be improved.
After revision:  To demonstrate ML-guided directed evolution, we introduce the steps required to build ML sequence-function models and use them to guide engineering, making recommendations at each stage. This review covers basic concepts relevant to using ML for protein engineering as well as the current literature and applications of this new engineering paradigm. ML methods accelerate directed evolution by learning from information contained in all measured variants and using it to select variants that are likely to be improved.