Accurate, high-resolution maps of bedrock outcrops are extremely valuable. The increasing availability of high-resolution imagery can be coupled with machine learning techniques to improve regional bedrock maps. This data release contains training data created for developing a machine learning model capable of identifying exposed bedrock across the entire Sierra Nevada Mountains (California, USA). The training data consist of 20 thematic rasters in GeoTIFF format, where image labels represent three categories: rock, not rock, and no data. These training data labels were created using 0.6-m imagery from the National Agriculture Imagery Program (NAIP) acquired in 2016. Eight existing labeled sites were available from Petliak et al. (2019), an earlier effort. We further revised those labels for improved accuracy and created additional 12 reference sites following the same protocol of semi-manual mapping in Petliak et al. (2019). A machine learning model (https://github.com/nasa/delta) was trained and tested based on these image labels as detailed in Shastry et al. (in review). The trained model was then used to map exposed bedrock across the entire Sierra Nevada region using 2016 NAIP imagery, and this data release also includes these model outputs. The model output gives the likelihood (from 0 to 255) that each pixel is bedrock, and not a direct binary classification. The associated publication used a threshold of 50%, or pixel value 127, where all pixel values 127 or higher are classified as rock and less than as not rock.