Machine learning components are rapidly being integrated across nearly every business sector, with their importance continuing to grow. However, the engineering practices behind building these systems are still not as well understood as those for traditional software. On this page, you will find a comprehensive list of 40 checks, organized into two main categories and 16 subcategories. These checks are designed to support software architects in tackling the unique challenges of ML-intensive systems, while also offering valuable guidance to practitioners and researchers on key areas for future development and improvement.