A model service mesh provides a scalable, high-performance infrastructure to manage, deploy, and schedule multiple model services. It simplifies model deployment, version control, routing, and load balancing for inference requests.
What is a model service mesh
A model service mesh is an architectural pattern used to deploy and manage machine learning model services in a distributed environment.
A model service mesh deploys models as scalable services and uses the mesh to manage and route them. The mesh abstracts model services into orchestrable and scalable units, which simplifies deployment, scaling, and version control. It also provides core features such as load balancing, auto scaling, and fault recovery to ensure high availability and reliability. Models can be automatically scaled and load balanced based on the inference request payload.
A model service mesh also provides advanced features such as traffic splitting, A/B testing, and phased releases for greater control over model service traffic. You can switch between and roll back different model versions with ease. The mesh supports dynamic routing, which routes requests to the appropriate model service based on request properties such as model type, data format, or other metadata.
Developers can deploy, manage, and scale machine learning models through the mesh, which provides high availability, elasticity, and flexibility for various business scenarios.