Seven chapters in, you can read a counter, diagnose a pause storm, spot an oversubscribed spine, calculate whether a topology supports AllReduce at line rate. Every tool is in place.
Then an ML engineer sends you a Slack message: "Training is stuck at 60% expected throughput. NCCL logs are spamming timeouts. No idea what the network is doing."
This is where the last seven chapters have to become a single coherent diagnostic story -- because the ML engineer cannot read ethtool -S eth0, and you cannot read a training loss curve. The translation layer between those two worlds is NCCL: the library that converts "AllReduce these gradient tensors" into actual RDMA operations on actual NICs, and the library that reports back when the fabric fails to support those operations.
After this chapter, when an ML engineer shows you a NCCL log with timeout errors and degraded busbw, you will know exactly which CLI commands to run, in what order, and how to connect what you find to what the training job experienced.