Model Serving Patterns

Serving ML Models

Different patterns for different needs.

Batch Inference

Scheduled predictions on accumulated data. Simple, cost-effective. Not real-time.

Real-Time Inference

API for individual predictions. Requires low latency. Flask, FastAPI for simple cases.

Streaming Inference

Process data streams. Complex, but handles high throughput. Kafka + Flink.

Edge Deployment

On-device inference. TensorFlow Lite, ONNX Runtime. Limited resources.

Key Takeaways

1. Batch for offline, real-time for online
2. Streaming for high throughput
3. Edge for low latency/offline