Batch ML Scoring to Real-Time Model Serving Blueprint

What and Why

Batch scoring computes predictions on a schedule and stores them. It is simple but stale: a user action minutes ago isn't reflected until the next run. Real-time serving exposes the model behind an API so predictions use fresh inputs at request time. This unlocks use cases like fraud checks, recommendations, and dynamic pricing that batch cannot serve.

This blueprint stands up an online model-serving path alongside batch and migrates traffic once latency and accuracy are proven.

Phases

Assessment. Identify scores that need freshness and have a latency budget (for example p99 under 100 ms). Confirm which features can be computed or fetched online.

Serving design. Choose a model server (KServe, Triton, Seldon, or a framework server). Define the request/response contract (REST/gRPC), batching, and autoscaling. Set SLOs for latency and availability.

Feature online store. Stand up an online feature store (Redis-backed) so the serving path reads the same features used in training. Establish a pipeline that keeps online and offline features consistent to avoid training/serving skew.

Shadow deployment. Run the online model in shadow: it scores live traffic but its output isn't used. Compare against batch outputs and ground truth.

Cutover. Canary real traffic to the online model, watch latency and accuracy, and ramp up. Keep batch as a fallback initially.

Key Risks and Mitigations

• Latency budget: model and feature lookups must fit the SLO. Use a low-latency feature store, request batching, and a compiled/optimized model (ONNX, TensorRT) where needed.
• Training/serving skew: features computed differently online and offline degrade accuracy. Share feature definitions via a feature store and monitor distributions.
• Data consistency: online features may lag events. Stream updates (Kafka) into the online store and monitor freshness.

Recommended Tooling

KServe/Triton/Seldon for serving on Kubernetes, a feature store (Feast) with a Redis online layer, Kafka for streaming feature updates, Prometheus for latency/SLO monitoring, and drift-detection on inputs and predictions.

Success Metrics

Measure online scoring p99 latency against the SLO, feature freshness, prediction accuracy versus batch and ground truth, and availability.

Prerequisites

A trained model, defined features with offline lineage, a Kubernetes platform, and agreed latency/availability SLOs.