What is distributed hyperparameter optimization at scale?

Distributed hyperparameter optimization parallelizes search across multiple compute nodes.

Hyperparameter optimization at scale uses distributed frameworks to run multiple training jobs concurrently. Techniques include Bayesian optimization, random search, evolutionary algorithms, and bandit-based pruning (e.g., Hyperband). Distributed systems allocate compute resources dynamically to maximize search efficiency while minimizing cost.