Mlops

Running AI workloads in containers presents unique challenges that traditional web application patterns don’t address. GPU scheduling, model caching, and bursty inference traffic all require thoughtful architecture. Here’s what actually works in production. The GPU Scheduling Problem Standard Kubernetes scheduling assumes CPU and memory are your primary constraints. When you add GPUs to the mix, everything changes. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 apiVersion: v1 kind: Pod metadata: name: inference-server spec: containers: - name: model image: my-registry/llm-server:v1.2 resources: limits: nvidia.com/gpu: 1 memory: "32Gi" requests: nvidia.com/gpu: 1 memory: "24Gi" The naive approach—one GPU per pod—works until you realize GPUs cost $2-4/hour and sit idle between requests. MIG (Multi-Instance GPU) and time-slicing help, but they introduce complexity: ...

Running AI inference in the cloud is easy until it isn’t. The moment you need real-time responses — autonomous vehicles, industrial quality control, AR applications — that 50-200ms round trip becomes unacceptable. Edge computing puts the model where the data lives. Here’s how to architect AI inference at the edge without drowning in complexity. The Latency Problem A typical cloud inference call: Capture data (camera, sensor) → 5ms Network upload → 20-100ms Queue wait → 10-50ms Model inference → 30-200ms Network download → 20-100ms Action → 5ms Total: 90-460ms ...