Why do large-scale ML systems prefer matrix factorization methods over direct computation?

Because factorization improves numerical stability and computational efficiency.

Direct matrix operations like inversion are expensive and unstable. Factorizations like LU, QR, and SVD break problems into simpler structured components, reducing computational complexity and improving stability.