Model Training Infrastructure and Experimentation Questions

Design infrastructure and workflows to train machine learning models at scale and enable rapid experimentation. Core areas include distributed training strategies such as data parallelism model parallelism and pipeline parallelism; hardware and instance selection including graphics processing units and tensor processing units; efficient resource scheduling and autoscaling for training; hyperparameter tuning at scale using grid search random search and Bayesian optimization; experiment and metadata tracking, reproducibility and checkpointing, resume and fault tolerance strategies; pipeline automation, containerized reproducible training environments, dataset management, and trade offs between training speed cost and model quality to support iterative model development.