How do you design resilient Azure ML inference architectures?

Resilient inference architectures use autoscaling, load balancing, traffic splitting, failover mechanisms, monitoring, and blue-green deployment strategies.

Production inference systems must handle failures gracefully while maintaining low latency and high availability.

Resilient Azure ML architectures commonly include:

Inference resilience is critical because production systems often operate under unpredictable workloads.…