Reducing model development time and enabling fast iteration may increase the likelihood of success for many ML projects. To make model training and experimentation faster and more efficient without incurring exorbitant costs, developed a scalable training environment using TorchElastic on Kubernetes. He discusses how TorchElastic, which makes training jobs resilient to node interruptions, enables to keep costs low by leveraging spot instances. Their team further streamlined model building by creating a simple CLI tool that researchers may use to launch, monitor, and manage training jobs.