Scaling machine learning models in distributed computing environments presents challenges that stem from the need to process large datasets and execute complex algorithms efficiently. Distributed computing is leveraged to manage data volumes that exceed the memory capacity of a single machine and to parallelize computations for complex algorithms, ensuring timely results. While hardware is a factor, the broader challenges include distributing tasks, parallelizing operations, and coordinating across multiple nodes. Thus, the interplay between large datasets, algorithmic complexity, and efficient parallelization stands as the core challenge in distributed machine learning at scale.