Ray Serve
Definition
Deploy models as scalable services.
Ray Serve is a serving library in the Ray ecosystem that provides model and Python applications as services and organizes their execution.
Simply explained
A distributor accepts orders and forwards them to appropriate computing stations.
Why it matters in practice
Multiple model steps can be connected in a controlled operational workflow.
Example from business
A hypothetical team provides classification and subsequent text processing via a service.
Limits and pitfalls
- Scaling requires resource planning, monitoring, and appropriate load testing.
Origin & history
Ray Serve is part of the Ray ecosystem for distributed computing.
Use in mid-sized companies
- First clarify load profile and operational responsibility.
Frequently asked questions
Does Ray Serve train the models?
Its focus is on their deployment and execution.
Sources
Editorially reviewed on: