Machine learning (ML) is rapidly emerging as an important tool for materials discovery. In this talk, we will address key considerations involved in applying ML to materials design problems, including: using sequential learning for inverse design of materials with target properties; quantifying model performance when extrapolating to new chemistries (rather than interpolating among well-known chemistries); and optimizing the design space (i.e., the set of candidates to search with ML).