How does PCA affect memory usage in large-scale ML pipelines?

PCA reduces memory usage by compressing high-dimensional data into fewer components.

In large datasets, storing full feature matrices is expensive. PCA reduces dimensionality, which reduces both storage and downstream memory consumption. However, PCA computation itself can be memory-intensive due to covariance or SVD operations, especially before reduction.