Principle:Kubeflow Kubeflow Serve Model
| Knowledge Sources | |
|---|---|
| Domains | MLOps, Model Serving, Inference |
| Last Updated | 2026-02-13 00:00 GMT |
Overview
Serve Model is the principle of deploying trained models as production-grade, auto-scaling inference endpoints that provide real-time predictions via standardized API protocols.
Description
Training a model produces artifacts; serving a model transforms those artifacts into a live service that applications can query for predictions. Model serving is the bridge between the ML development lifecycle and production value delivery. A well-designed serving infrastructure must address several concerns simultaneously: low-latency inference, automatic scaling (including scale-to-zero for cost efficiency), canary deployments for safe rollouts, request batching for throughput optimization, model explainability, and pre/post-processing transformations.
This principle defines the practice of deploying models through a managed serving platform that abstracts away the infrastructure complexity of load balancing, autoscaling, traffic routing, and protocol handling. The serving platform should support multiple model frameworks (PyTorch, TensorFlow, scikit-learn, XGBoost, ONNX, custom runtimes) and expose standardized prediction APIs (REST and gRPC) conforming to the Open Inference Protocol.
Within the Kubeflow ecosystem, KServe (formerly KFServing) is the model serving platform. KServe provides the InferenceService CRD as a declarative API for deploying models with built-in support for predictor runtimes, transformer components (pre/post-processing), explainer components (model interpretability), canary traffic splitting, and GPU autoscaling via Knative or Kubernetes HPA.
Usage
Apply this principle when:
- A trained and registered model must be made available for real-time inference by applications or users.
- The deployment must auto-scale based on request load, including scale-to-zero during idle periods.
- Canary or blue-green deployment strategies are required for safe model updates in production.
- Pre-processing (feature transformation, tokenization) or post-processing (label decoding, calibration) must be co-deployed with the model.
- Model explainability (e.g., SHAP, LIME) must be available alongside predictions.
- Multiple model frameworks need a unified serving API and management interface.
Theoretical Basis
Model serving follows a structured deployment and lifecycle process:
Step 1: Runtime Selection
- Select the appropriate serving runtime based on the model format and framework.
- Built-in runtimes are available for common frameworks (TorchServe for PyTorch, TensorFlow Serving, Triton for multi-framework, MLServer for scikit-learn/XGBoost).
- Custom runtimes can be used for specialized model types or serving logic.
Step 2: Inference Graph Design
- Define the inference graph components: predictor (core model inference), transformer (pre/post-processing), and optionally explainer (interpretability).
- The predictor receives processed input and returns raw model output.
- The transformer handles input/output transformations that should not be embedded in the model itself.
- The explainer provides feature attributions or counterfactual explanations alongside predictions.
Step 3: Resource and Scaling Configuration
- Set resource requests and limits (CPU, memory, GPU) for each component.
- Configure autoscaling parameters: minimum and maximum replicas, target concurrency, and scale-down delay.
- Scale-to-zero is enabled by default for cost efficiency during low-traffic periods.
Step 4: Traffic Management
- Deploy the new model version alongside the existing one.
- Configure canary traffic splitting to route a percentage of traffic to the new version.
- Monitor prediction quality and latency on the canary before promoting to full traffic.
- Roll back by shifting traffic entirely to the previous version if issues are detected.
Step 5: Endpoint Exposure
- The serving platform provisions an inference endpoint (REST and/or gRPC).
- Clients send prediction requests conforming to the Open Inference Protocol.
- The platform handles request routing, load balancing, batching, and response delivery.