Implement automatic shutdown policies for idle Compute Engine instances to reduce costs associated with unused resources.

Use Preemptible VMs for batch processing and other non-critical ML workloads that can tolerate interruptions, as they offer significant cost savings over regular instances.

Deploy monitoring tools to track and optimize GPU utilization across ML workloads, given the premium cost of GPUs, to ensure efficient use of these resources.

Adopt a policy of treating AI Platform Notebooks as ephemeral resources, deleting them when not in active use to avoid incurring costs for reserved but unused capacity.

All of the above strategies are part of Google's recommended best practices for optimizing costs in ML projects on GCP.