How do you architect cost-optimized Azure ML platforms at scale?

Cost-optimized Azure ML architectures use autoscaling, spot VMs, workload scheduling, shared infrastructure, efficient storage strategies, and observability-driven optimization.

Machine learning infrastructure costs can grow rapidly due to GPU consumption, large datasets, distributed training, and continuous inference workloads.

A cost-optimized Azure ML architecture focuses on:

3.…