Can an external SSD really cope with machine learning for an SA buyer?

ADATA LEGEND 900 1TB at R948 suits an SA buyer who wants a sensible ssd starting point for machine learning project files, model checkpoints, and fast local scratch storage without jumping straight to premium pricing. The key is to treat external storage as a working drive for datasets and transfers, while keeping the heaviest training expectations realistic.

Short answer for ML storage

WD Blue SN5000 at R998 is the practical middle choice if the brief is a dependable SSD for code folders, notebooks, exports, and repeat file movement around an ML workspace. Its SN5000 model line gives you a named option just above the cheapest listed pick, which matters when the drive will be used daily instead of sitting as archive storage.

ADATA LEGEND 900 1TB at R948 is the value pick because the listed 1TB capacity is easy to plan around. For a student desk or creator desk, that means room for datasets, Python environments, labelled image folders, and saved outputs without paying Samsung 990 PRO money upfront.

SA creator desk reality for machine learning

Samsung 990 PRO at R4,648 makes more sense when the drive is part of a serious workstation routine: large local datasets, frequent experiment saves, and project handovers that need a higher-end SSD choice. It costs far more than the ADATA LEGEND 900 1TB at R948, so it should be chosen for a heavier workload, not just because the word machine learning sounds demanding.