Model Serving
The infrastructure and processes for deploying trained models to make predictions in production environments.
In-Depth Explanation
Model serving is the process of deploying trained ML models to production environments where they can receive requests and return predictions. It's the bridge between model development and real-world use.
Model serving components:
- Model server: Hosts model, handles requests
- API layer: Exposes prediction endpoints
- Load balancing: Distributes traffic
- Scaling: Handles demand changes
- Monitoring: Tracks performance, drift
Serving approaches:
- REST APIs (most common)
- gRPC (high performance)
- Batch prediction (scheduled)
- Edge deployment (on-device)
- Streaming (real-time)
Tools:
- TensorFlow Serving
- TorchServe
- Triton (NVIDIA)
- BentoML, MLflow
- Cloud ML services
Business Context
Model serving turns ML models into usable services. Good serving infrastructure ensures reliability, performance, and scalability.
How Clever Ops Uses This
We deploy and manage ML model serving for Australian businesses, ensuring reliable, scalable, and monitored production AI systems.
Example Use Case
"Deploying a fraud detection model behind an API that scores transactions in real-time, handling thousands of requests per second with sub-100ms latency."