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Author: shaneutt

README.md

@@ -8,9 +8,9 @@ This project offers tools for AI Inference, enabling developers to build [Infere
 
 AI/ML is changing rapidly, and [Inference] goes beyond basic networking to include complex traffic routing and optimizations. Below are key terms used within this project:
 
-- **Scheduler**: Makes decisions about which endpoint is optimal (best cost / best performance) for an inference request based on `Metrics and Capabilities` from Model Serving Platforms.
+- **Scheduler**: Makes decisions about which endpoint is optimal (best cost / best performance) for an inference request based on `Metrics and Capabilities` from [Model Serving](/docs/proposals/003-model-server-protocol/README.md).
 - **Metrics and Capabilities**: Data provided by model serving platforms about performance, availability and capabilities to optimize routing. Includes things like [Prefix Cache] status or [LoRA Adapters] availability.
-- **Endpoint Picker**: A `Scheduler` combined with `Metrics and Capabilities` systems is often referred to together as an `Endpoint Picker (EPP)`.
+- **Endpoint Selector**: A `Scheduler` combined with `Metrics and Capabilities` systems is often referred to together as an [Endpoint Selection Extension](https://gateway-api-inference-extension.sigs.k8s.io/#endpoint-selection-extension) (this is also sometimes referred to as an "endpoint picker").
 - **Inference Gateway**: A proxy/load-balancer which has been coupled with a `Endpoint Picker`. It provides optimized routing and load balancing for serving generative Artificial Intelligence (AI) workloads. It simplifies the deployment, management, and observability of AI inference workloads.
 
 For deeper insights and more advanced concepts, refer to our [proposals](/docs/proposals).