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.

翻译：我们概述了在数据科学预测生命周期各阶段开发和采用自动化所面临的技术和文化挑战,把重点限制在有条理的数据集监督的学习上,此外,我们审查流行的开放源码Python工具,以采用共同的解决方案模式应对自动化挑战,并着重指出我们认为仍需要取得进展的差距。