One factor for the rapid growth of the field is the ability for materials machine learning research to rapidly build upon past work such as databases, software, ML methods, or domain-specific techniques. New research papers can build upon data sets from prior papersbypassing expensive data collection and focusing on method developmentor transfer methods developed by the computer science community to the materials domain. Such methods, packaged in reusable software libraries, can then be applied directly to tackling specific materials problems, often with minimal additional method development, data collection, or software programming. This review is divided into three sections. In the first section, it presents an analysis of the cross-fertilization between machine learning methods, materials science methods, data and software by analyzing common citations between papers. The second section presents a quantitative analysis of the amount of progress achieved in a particular subfield of materials machine learningstructure-based property prediction. The final section presents remaining challenges and opportunities, particularly in the areas of data size and complexity, extrapolation, interpretation, access, and relevance.