Master DNS and service discovery for distributed ML systems - DNS resolution chains, Kubernetes CoreDNS, Consul service mesh, etcd coordination, and how ML serving clusters register and find model endpoints dynamically.
Learn gRPC and Protocol Buffers for high-performance ML inference APIs - from protobuf wire format to bidirectional streaming, interceptors, health checks, and production deployment patterns.
Understand HTTP/3 and QUIC - how QUIC solves TCP head-of-line blocking with UDP-based multiplexing, 0-RTT connection establishment, TLS 1.3 integration, and what it means for ML inference serving latency.
Master Apache Kafka for ML data pipelines - topics, partitions, consumer groups, exactly-once semantics, real-time feature computation, prediction logging, and production patterns for ML platforms.
Master distributed training network debugging - NCCL error diagnosis, AllReduce communication patterns, bandwidth testing with iperf3 and nccl-tests, RDMA diagnostics, and profiler-based timeline analysis for PyTorch DDP.
Comprehensive network security for ML infrastructure - mTLS service authentication, Kubernetes network policies, eBPF with Cilium, secrets management with Vault, zero-trust networking, and ML-specific threats including model theft and prompt injection.
Master service mesh architecture and load balancing for ML serving - Istio, Envoy, traffic management, mTLS, canary deployments, circuit breaking, and Kubernetes networking for production AI systems.
Master the networking layer that underpins every distributed training run and ML serving system - from TCP handshakes to jumbo frames and congestion control algorithms used in modern GPU clusters.
