This talk will outline how to make climate science datasets and models accessible for machine learning. The focus will be on climate science challenges and opportunities associated with two distinct projects, 1) EnviroNet: a project focused on bridging gaps between geoscience and machine learning research through a global data repository of ImageNet analogs and AI challenges, and 2) a Mila project on changing people's minds and behavior through visualization of future extreme climate events. The discussion related to EnviroNet will be on how datasets and climate science problems can be framed for the machine learning research community at large. The discussion related to the Mila project will include climate science forecast model prototype developments in progress for accurately visualizing future extreme climate impacts of events such as floods, that particularly impact individual's neighborhoods and households.