What is the core idea behind model serving?
Serving is how models become products.
How does model serving differ from related concepts?
| Concept | Difference |
|---|---|
| Training vs Serving | Training builds. Serving delivers |
| Serving vs Inference | Inference is prediction. Serving manages delivery |
| Serving vs API | APIs expose the model. Serving powers the backend |
How does model serving work?
- A trained model is deployed to infrastructure
- Inputs are received via API or system calls
- The model performs inference
- Outputs are returned to the user or system
What are the limitations of model serving?
- Latency issues
- Scaling under high demand
- Infrastructure costs
Why is model serving important?
Without serving, models cannot be used in real-world applications. Serving infrastructure determines the reliability, speed, and cost of AI services.
How is model serving used in practice?
Used in chat apps, recommendation systems, fraud detection, and enterprise AI tools. Major platforms include AWS SageMaker, Google Vertex AI, and Azure ML.
Frequently Asked Questions
What makes serving difficult at scale?
Handling high traffic, maintaining low latency, and optimizing cost-performance trade-offs.
Is serving only real-time?
No. It can be real-time or batch depending on the use case.