curl http://127.0.0.1:8082/metrics

In case you are looking into exporting the logged metrics, please refer to this example that uses mtail to export metrics to Prometheus. Tracking these metrics in a dashboard allows you to monitor performance regressions that may have been sporadic or hard to spot during an offline benchmark run.
What to consider for tuning performance of a model in production
The workflow suggested in Fig 1, is the general idea on how to approach model deployment in production with Torchserve.
In many cases serving models in production is optimized based on throughput or latency service level agreement (SLA)s. Usually real-time applications are more concerned about latency whereas off-line applications may care more about higher throughput.