I would say applied statistics draws a line just prior to implementation concerns (say, real-world resource usage measured in time, space and energy) whereas these would be fully within scope and of interest in machine learning.

As an example, applied statistics could provide a useful approach to a vision/image recognition problem, and this approach might be provably unrealizable in practice using real-world execution units (e.g. CUDA cores). Nonetheless, it might still be a very worthwhile theoretical result in applied statistics, although of no immediate interest within ML except to hint at potential new area of research.