Running ML training jobs on a cluster of GPU nodes is essential for handling large datasets and models, but it also introduces risks due to failures during training. Anyscale's elastic training feature allows practitioners to train models in reasonable time frames while ensuring continuous execution despite hardware failures or node preemptions, avoiding idle or wasted time. With this feature, users can configure jobs to run on spot instances, which can reduce costs by up to 60%, and automatically scale up when more nodes become available, maintaining the largest possible cluster for timely results. Implementing elastic training in Anyscale requires minimal code changes, allowing developers to adapt their existing code with a simple change in scaling configuration.