How would you migrate an on-premises ML training workload to AWS?

Move data first with DataSync or Snowball, containerise training so it runs identically in both places, validate parity by reproducing a known model, then migrate incrementally — starting with new experiments rather than cutting over production.

Migrations fail on data gravity and on unvalidated parity, not on compute.

Phase 1 — Assess. Inventory current data volume and growth, GPU hours and utilisation, framework and CUDA versions, and any licensed or proprietary dependencies. Utilisation is the number that matters: an on-prem cluster at 25% average utilisation is the strongest argument for the move, because you stop paying for idle.…