  We present an expository overview of technical and cultural challenges to the
development and adoption of automation at various stages in the data science
prediction lifecycle, restricting focus to supervised learning with structured
datasets. In addition, we review popular open source Python tools implementing
common solution patterns for the automation challenges and highlight gaps where
we feel progress still demands to be made.
