An organization needs to increase the speed at which they can train machine learning models. Which domain-specific hardware is designed for this task?

Bare Metal Solution provides dedicated physical servers for workloads requiring low-latency hardware access, but is not specialized for ML acceleration.

Preemptible or Spot VMs are cost-reduced compute instances that can be interrupted; they are not designed specifically for ML acceleration.

Cloud TPUs are domain-specific hardware accelerators built by Google to dramatically speed up ML workloads, particularly large-scale neural network training. They are optimized for the tensor math used in frameworks like TensorFlow and JAX, offering significantly higher throughput for model training than general-purpose CPUs or even GPUs at scale.

Containers are a software packaging and deployment technology, not hardware designed to accelerate ML training.