Designing on-demand ML training infrastructure on AWS: A practical architecture guide
Machine learning projects do not depend only on good models. They also depend on the infrastructure behind them. Training workloads are expensive, unpredictable, and often inefficient when run on static environments. As organizations scale their AI efforts, the ability to provision training infrastructure on demand becomes critical for both cost control and speed of experimentation
