Model Serving Infrastructure
Getting a model into a notebook is the easy part. Serving it at scale with sub-100ms latency, autoscaling, and zero-downtime updates — that's infrastructure work. We build the serving layer so your models handle real traffic reliably.
Need this done for your project?
We implement, you ship. Async, documented, done in days.
Serving Framework Selection
We pick the right serving framework for your models: TensorFlow Serving for TF models, Triton Inference Server for multi-framework support, TorchServe for PyTorch, or KServe for Kubernetes-native serving. Each has tradeoffs in batching, GPU sharing, and protocol support — we match the tool to your workload, not the other way around.
Autoscaling & Load Management
Model endpoints autoscale based on request queue depth, GPU utilization, or custom metrics. Kubernetes HPA or KEDA handles scaling decisions. We configure request batching to maximize GPU throughput — grouping inference requests reduces per-request latency and increases hardware utilization by 3-5x on batch-friendly models.
Canary Deployments & A/B Routing
New model versions deploy as canary releases — 5% traffic initially, scaling up as metrics confirm performance. Istio or Linkerd handle traffic splitting at the mesh level. If the new model's latency spikes or accuracy drops, traffic automatically routes back to the stable version. No manual rollbacks at 2am.
Latency & Performance Monitoring
Every inference request gets traced end-to-end: preprocessing time, model inference, postprocessing. P50, P95, and P99 latencies are tracked per model version. Alerts fire when latency exceeds SLO thresholds. You get Grafana dashboards showing request throughput, error rates, and GPU memory consumption per endpoint.
Why Anubiz Engineering
Ready to get started?
Skip the research. Tell us what you need, and we'll scope it, implement it, and hand it back — fully documented and production-ready.