From Kubernetes Dreams to Docker Reality: Building an ML Inference Cluster on Jetson Nano (Part 1 of 5)
From Kubernetes Dreams to Docker Reality: Building an ML Inference Cluster on Jetson Nano I set out with a clear goal: stand up a four-node Jetson Nano cluster running k3s, connect it to Splunk’s Deep Learning Toolkit (DLTK), and use the Nano GPUs to serve ML model inference for security analytics use cases — specifically DNS tunneling detection and lateral movement identification from host-based firewall data. The plan was reasonable on paper. Kubernetes gives you scheduling, self-healing, and a clean abstraction over bare hardware. Jetson Nanos are purpose-built for edge AI workloads. Splunk DLTK provides a framework for connecting ML models to security event streams. What could go wrong? ...