Incorporating machine learning (ML) components into software products raises new software-engineering challenges and elevates already existing challenges. Many researchers have invested significant effort into understanding the challenges of industry practitioners working on building products with ML components through interviews and surveys with practitioners. With the intention to aggregate and present their collective findings, we conduct a meta-summary study: We collect 50 relevant papers that together interacted with over 4758 practitioners using guidelines for systematic literature reviews and subsequently group and organize the over 500 mentions of challenges within those papers. We highlight the most commonly reported challenges and how this meta-summary will be a useful resource for the research community to prioritize research and education in this field.