docs(multiple pages): added page descriptions so that they are discoverable by LLMs (#7025)

Rajakavitha1 · Rakavitha Kodhandapani · paulb-seldon · web-flow · commit cf26eb7caa70 · 2025-12-22T15:51:37.000+01:00
* added descriptions

* Update description in SUMMARY.md

* Update README.md

* Fix typo in license generation description

* Update description for CIFAR-10 example tutorial

* Update local-examples.md

* Update description for cyclic pipeline documentation

* Update README.md

* Update README.md

* Update README.md

* Update infrastructure-setup.md

* Update load-testing.md

* Update description for Seldon Core 2 documentation

* Update description for Seldon Core Autoscaling

* Fix description wording in upgrading.md

---------

Co-authored-by: Rakavitha Kodhandapani &lt;seldon@SELIN002.local&gt;
Co-authored-by: paulb-seldon &lt;141156400+paulb-seldon@users.noreply.github.com&gt;
diff --git a/docs-gb/SUMMARY.md b/docs-gb/SUMMARY.md
@@ -1,3 +1,7 @@
+---
+description: Table of Contents for Seldon Core 2 documentation.
+---
+
 # Table of contents
 
 ## About
diff --git a/docs-gb/apis/README.md b/docs-gb/apis/README.md
@@ -1,3 +1,7 @@
+---
+description: APIs in Seldon Core 2.
+---
+
 # APIs
 
 Seldon provides APIs for management and inference.
diff --git a/docs-gb/apis/internal/README.md b/docs-gb/apis/internal/README.md
@@ -1,3 +1,7 @@
+---
+description: Internal APIs in Seldon Core 2.
+---
+
 # Internal APIs
 
 Seldon provides some internal gRPC services it uses to manage components.
diff --git a/docs-gb/cli/README.md b/docs-gb/cli/README.md
@@ -1,3 +1,7 @@
+---
+description: Seldon Core 2 CLI documentation.
+---
+
 # CLI
 
 Seldon provides a CLI to allow easy management and testing of Model, Experiment, and Pipeline resources.
@@ -152,4 +156,4 @@ e.g.:
 
 ```
 ./download-k8s-certs.sh seldon-mesh seldon-scheduler-client
-```
+```
diff --git a/docs-gb/cli/seldon.md b/docs-gb/cli/seldon.md
@@ -1,3 +1,7 @@
+---
+description: seldon commands in Seldon Core 2.
+---
+
 ## seldon
 
 
diff --git a/docs-gb/development/README.md b/docs-gb/development/README.md
@@ -1,3 +1,7 @@
+---
+description: Development in Seldon Core 2.
+---
+
 # Development
 
 ## Development Requirements
diff --git a/docs-gb/development/licenses.md b/docs-gb/development/licenses.md
@@ -1,3 +1,7 @@
+---
+description: 3rd party license generation in Seldon Core 2.
+---
+
 # 3rd part license generation
 
 We use the following tools and processes for generationg 3rd party licenses.
diff --git a/docs-gb/development/release.md b/docs-gb/development/release.md
@@ -1,3 +1,7 @@
+---
+description: Release process in Seldon Core 2.
+---
+
 # Release
 
 This document summarizes the release process for Seldon Core 2.\
diff --git a/docs-gb/examples/README.md b/docs-gb/examples/README.md
@@ -1,3 +1,7 @@
+---
+description: Examples in Seldon Core 2.
+---
+
 # Examples
 
 ## Workflows
diff --git a/docs-gb/examples/batch-examples-local.md b/docs-gb/examples/batch-examples-local.md
@@ -1,3 +1,7 @@
+---
+description: Batch Inference examples (local) in Seldon Core 2.
+---
+
 # Batch Inference examples (local)
 
 This example runs you through a series of batch inference requests made to both models and pipelines running on Seldon Core locally.
diff --git a/docs-gb/examples/cifar10.md b/docs-gb/examples/cifar10.md
@@ -1,3 +1,7 @@
+---
+description: Tutorial of deploying an image classifier with drift and outlier monitoring in Seldon Core 2.
+---
+
 # Production image classifier with drift and outlier monitoring
 
 Run these examples from the `samples/examples/image_classifier` folder.
diff --git a/docs-gb/examples/local-examples.md b/docs-gb/examples/local-examples.md
@@ -1,3 +1,7 @@
+---
+description: Examples of Seldon Core 2 used in a local environment.
+---
+
 # Local examples
 
 ### SKLearn Model
diff --git a/docs-gb/examples/pandasquery.md b/docs-gb/examples/pandasquery.md
@@ -1,3 +1,7 @@
+---
+description: Conditional pipeline with pandas query model in Seldon Core 2.
+---
+
 # Conditional pipeline with pandas query model
 
 The model is defined as an MLServer custom runtime and allows the user to pass in a custom\
diff --git a/docs-gb/examples/pipeline-cyclic.md b/docs-gb/examples/pipeline-cyclic.md
@@ -1,3 +1,7 @@
+---
+description: Cyclic Inference Pipeline in Seldon Core 2.
+---
+
 # Cyclic Pipeline
 
 Learn how to deploy a cyclic pipeline using Core 2. In this example, you'll build a simple counter that begins at a user-defined starting value and increments by one until it reaches 10. If the starting value is already greater than 10, the pipeline terminates immediately without running.
diff --git a/docs-gb/examples/pipeline-examples.md b/docs-gb/examples/pipeline-examples.md
@@ -1,3 +1,7 @@
+---
+description: Pipeline examples in Seldon Core 2.
+---
+
 # Pipeline examples
 
 These examples illustrates a series of Pipelines showing of different ways of combining flows of\
diff --git a/docs-gb/examples/pipeline-to-pipeline.md b/docs-gb/examples/pipeline-to-pipeline.md
@@ -1,3 +1,7 @@
+---
+description: Pipeline to pipeline examples in Seldon Core 2.
+---
+
 # Pipeline to pipeline examples
 
 This examples illustrates a series of Pipelines that are joined together.
diff --git a/docs-gb/examples/speech-to-sentiment.md b/docs-gb/examples/speech-to-sentiment.md
@@ -1,3 +1,7 @@
+---
+description: Huggingface speech to sentiment with explanations pipeline in Seldon Core 2.
+---
+
 # Huggingface speech to sentiment with explanations pipeline
 
 In this example we create a Pipeline to chain two huggingface models to allow speech to\
diff --git a/docs-gb/getting-started/README.md b/docs-gb/getting-started/README.md
@@ -1,3 +1,7 @@
+---
+description: Getting Started in Seldon Core 2.
+---
+
 # Getting Started
 
 {% hint style="info" %}
diff --git a/docs-gb/getting-started/kubernetes-installation/README.md b/docs-gb/getting-started/kubernetes-installation/README.md
@@ -1,3 +1,7 @@
+---
+description: Kubernetes Installation in Seldon Core 2.
+---
+
 # Kubernetes Installation
 
 ## Prerequisites
diff --git a/docs-gb/getting-started/kubernetes-installation/security/README.md b/docs-gb/getting-started/kubernetes-installation/security/README.md
@@ -1,3 +1,7 @@
+---
+description: Security in Seldon Core 2.
+---
+
 # Security
 
 Seldon can be run with secure control plane and data plane operations. There are three areas of concern:
diff --git a/docs-gb/getting-started/quickstart/quickstart.md b/docs-gb/getting-started/quickstart/quickstart.md
@@ -1,3 +1,6 @@
+---
+description: Learn how to deploy an AI application with Seldon Core 2.
+---
 # Quickstart: Deploy an AI Application with Seldon Core 2
 
 In this notebook, we will demonstrate how to deploy a production-ready AI application with Seldon Core 2. This application will have two components - an sklearn model and a preprocessor written in Python - leveraging Core 2 **Pipelines** to connect the two. Once deployed, users will have an endpoint available to call the deployed application. The inference logic can be visualized with the following diagram:
diff --git a/docs-gb/installation/advanced-configurations/README.md b/docs-gb/installation/advanced-configurations/README.md
@@ -1,2 +1,6 @@
+---
+description: Advanced Configurations in Seldon Core 2.
+---
+
 # Advanced Configurations
 
diff --git a/docs-gb/kubernetes/README.md b/docs-gb/kubernetes/README.md
@@ -1,3 +1,7 @@
+---
+description: Kubernetes in Seldon Core 2.
+---
+
 # Kubernetes
 
 Kubernetes provides the core platform for production use. However, the architecture in
diff --git a/docs-gb/kubernetes/confluent/README.md b/docs-gb/kubernetes/confluent/README.md
@@ -1,3 +1,7 @@
+---
+description: Leveraging Confluent Kafka with Seldon Core 2.
+---
+
 # Confluent
 
 Seldon Core 2 leverages Kafka for implementing data-centric inference Pipelines. This also means that Core 2 users have the ability of integrating with other products from the Kafka ecosystem, such as Schema Registry, Connect or ksqlDB in order to build custom data streaming, processing and logging solutions for machine learning workflows. 
diff --git a/docs-gb/kubernetes/resources/README.md b/docs-gb/kubernetes/resources/README.md
@@ -1,3 +1,6 @@
+---
+description: Learn about the Kubernetes Resources offered by Seldon Core 2.
+---
 # Resources
 
 For Kubernetes usage we provide a set of custom resources for interacting with Seldon.
diff --git a/docs-gb/kubernetes/resources/experiment.md b/docs-gb/kubernetes/resources/experiment.md
@@ -1,3 +1,7 @@
+---
+description: Experiments in Seldon Core 2.
+---
+
 # Experiments
 
 An Experiment defines a traffic split between Models or Pipelines. This allows new versions of models and pipelines to be tested.
diff --git a/docs-gb/kubernetes/resources/serverconfig.md b/docs-gb/kubernetes/resources/serverconfig.md
@@ -1,3 +1,7 @@
+---
+description: Server Config in Seldon Core 2.
+---
+
 # Server Config
 
 {% hint style="info" %}
diff --git a/docs-gb/kubernetes/service-meshes/README.md b/docs-gb/kubernetes/service-meshes/README.md
@@ -1,3 +1,7 @@
+---
+description: Using Service Meshes with Seldon Core 2.
+---
+
 # Service Meshes
 
 The Seldon models and pipelines are exposed via a single service endpoint in the install\
diff --git a/docs-gb/metrics/README.md b/docs-gb/metrics/README.md
@@ -1,3 +1,7 @@
+---
+description: Metrics in Seldon Core 2.
+---
+
 # Metrics
 
 There are two kinds of metrics present in Seldon Core 2:
diff --git a/docs-gb/models/README.md b/docs-gb/models/README.md
@@ -1,3 +1,7 @@
+---
+description: Models in Seldon Core 2.
+---
+
 # Models
 
 Models provide the atomic building blocks of Seldon. They represents machine learning models,\
diff --git a/docs-gb/models/parameterized-models/README.md b/docs-gb/models/parameterized-models/README.md
@@ -1,3 +1,7 @@
+---
+description: Parameterized Models in Seldon Core 2.
+---
+
 # Parameterized Models
 
 The Model specification allows parameters to be passed to the loaded model to allow customization. For example:
diff --git a/docs-gb/operational-monitoring/README.md b/docs-gb/operational-monitoring/README.md
@@ -1,3 +1,7 @@
+---
+description: Operational Monitoring in Seldon Core 2.
+---
+
 # Operational Monitoring
 
 Seldon Core 2 provides robust tools for tracking the performance and health of machine learning models in production.
diff --git a/docs-gb/performance-tuning/models/README.md b/docs-gb/performance-tuning/models/README.md
@@ -1,3 +1,7 @@
+---
+description: Model Performance Tuning in Seldon Core 2.
+---
+
 # Model Performance Tuning
 
 This section covers various aspects of optimizing model performance in Seldon Core 2, from initial load testing to infrastructure setup and inference optimization. Each subsection provides detailed guidance on different aspects of model performance tuning:
diff --git a/docs-gb/performance-tuning/models/inference.md b/docs-gb/performance-tuning/models/inference.md
@@ -1,3 +1,7 @@
+---
+description: Inference in Seldon Core 2.
+---
+
 # Inference
 
 When looking at optimizing the latency or throughput of deployed models or pipelines, it is important to consider different approaches to the execution of inference workloads. Below are some tips on different approaches that may be relevant depending on the requirements of a given use-case:
diff --git a/docs-gb/performance-tuning/models/infrastructure-setup.md b/docs-gb/performance-tuning/models/infrastructure-setup.md
@@ -1,4 +1,8 @@
-# Infrastructure Setup
+---
+description: Infrastructure Setup for Seldon Core 2 usage.
+---
+
+## Infrastructure Setup
 
 ## CPUs vs. GPUs
 
diff --git a/docs-gb/performance-tuning/models/load-testing.md b/docs-gb/performance-tuning/models/load-testing.md
@@ -1,3 +1,7 @@
+---
+description: Load testing Seldon Core 2.
+---
+
 # Load Testing
 
 Before looking to make changes to improve latency or throughput of your models, it is important to undergo load testing to understand the existing performance characteristics of your model(s) when deployed onto the chosen inference server (MLServer or Triton). The goal of load testing should be to understand the performance behavior of deployed models in saturated (i.e at the maximum throughput a model replica can handle) and non-saturated regimes, then compare it with expected latency objectives.
diff --git a/docs-gb/performance-tuning/models/model-artefacts.md b/docs-gb/performance-tuning/models/model-artefacts.md
@@ -1,3 +1,7 @@
+---
+description: Optimizing the model artefact in Seldon Core 2.
+---
+
 ## Optimizing the model artefact
 
 The speed at which an ML model can return results given input is based on the model’s architecture, model size, the precision of the model’s weights, and input size. In order to reduce the inherent complexity in the data processing required to execute an inference due to the attributes of a model, it is worth considering: 
diff --git a/docs-gb/performance-tuning/pipelines/README.md b/docs-gb/performance-tuning/pipelines/README.md
@@ -1,3 +1,7 @@
+---
+description: Pipeline Performance Tuning in Seldon Core 2.
+---
+
 # Pipeline Performance Tuning
 
 This section covers various aspects of optimizing pipeline performance in Seldon Core 2, from testing methodologies to Core 2 configuration. Each subsection provides detailed guidance on different aspects of pipeline performance tuning:
diff --git a/docs-gb/performance-tuning/pipelines/core-2-configuration.md b/docs-gb/performance-tuning/pipelines/core-2-configuration.md
@@ -1,3 +1,7 @@
+---
+description: Reducing Data Processing Overheads when using Seldon Core 2.
+---
+
 ## Reducing Data Processing Overheads
 
 When tuning performance for pipelines, reducing the overhead of Core 2 components responsible for data-processing within a pipeline is another aspect to consider. In Core 2, four components influence this overhead:
diff --git a/docs-gb/performance-tuning/pipelines/scalability-pipelines.md b/docs-gb/performance-tuning/pipelines/scalability-pipelines.md
@@ -1,3 +1,7 @@
+---
+description: Pipeline Scalability Guide in Seldon Core 2.
+---
+
 # Pipeline Scalability Guide
 
 Core 2 supports full **horizontal scaling** for the **dataflow engine**, **model gateway**, and **pipeline gateway**. Each service automatically distributes pipelines or models across replicas using consistent hashing, so you don’t need to manually assign workloads.
diff --git a/docs-gb/performance-tuning/pipelines/testing-pipelines.md b/docs-gb/performance-tuning/pipelines/testing-pipelines.md
@@ -1,3 +1,7 @@
+---
+description: Pipelines in Seldon Core 2.
+---
+
 # Testing Pipelines
 
 In order to understand how pipelines will behave in a production setting, it is first helpful to isolate testing to the individual models within the pipeline. By obtaining the maximum throughput for each model given the infrastructure each are running on (e.g. GPU specs) and their configurations (e.g. how many workers are used), users will gain a better understanding of which models might be limiting to the performance of the pipeline around it. Given the performance profiles of models within a pipeline, it is recommended to optimize for the desired performance of those individual models first (see [here](../models/)), ensuring they have the right number of replicas and are running on the right infrastructure in order to achieve the right level of performance. Similarly, once a model within a pipeline is identified as a bottleneck, refer back to the [models](../models/) section to optimize the performance of that model.
diff --git a/docs-gb/performance-tuning/readme.md b/docs-gb/performance-tuning/readme.md
@@ -1,3 +1,7 @@
+---
+description: Introduction in Seldon Core 2.
+---
+
 # Introduction
 
 In MLOps, system performance can be defined as how efficiently and effectively an ML model or application operates in a production environment. It is typically measured across several key dimensions, such as latency, throughput, scalability, and resource-efficiency. These factors are deeply connected: changes in configuration often result in tradeoffs, and should be carefully considered. Specifically, latency, throughput and resource usage can all impact each other, and the approach to optimizing system performance depends on the desired balance of these outcomes in order to ensure a positive end-user experience, while also minimising infrastructure costs.  
diff --git a/docs-gb/scaling/core-autoscaling.md b/docs-gb/scaling/core-autoscaling.md
@@ -1,3 +1,7 @@
+---
+description: Autoscaling with Seldon Core 2.
+---
+
 # Seldon Core Autoscaling
 
 Seldon Core provides native autoscaling features for both Models and Servers, enabling automatic scaling based on inference load. The diagram below depicts an autoscaling implementation that uses both Model and Server autoscaling features native to Seldon Core (i.e. this implementation doesn't leverage HPA for autoscaling, an approach we cover [here](hpa-overview.md))
diff --git a/docs-gb/scaling/model-hpa-autoscaling.md b/docs-gb/scaling/model-hpa-autoscaling.md
@@ -1,3 +1,7 @@
+---
+description: Configuring HPA manifests in Seldon Core 2.
+---
+
 
 ## Configuring HPA manifests
 
diff --git a/docs-gb/upgrading.md b/docs-gb/upgrading.md
@@ -1,3 +1,6 @@
+---
+description: Learn about upgrading to the latest versions of Seldon Core 2.
+---
 # Upgrading
 
 ## Upgrading from 2.9 - 2.10
diff --git a/docs-gb/user-guide/data-science-monitoring/README.md b/docs-gb/user-guide/data-science-monitoring/README.md
@@ -1,2 +1,6 @@
+---
+description: Data Science Monitoring in Seldon Core 2.
+---
+
 # Data Science Monitoring
 
diff --git a/docs-gb/user-guide/inference/README.md b/docs-gb/user-guide/inference/README.md
@@ -1,2 +1,6 @@
+---
+description: Inference in Seldon Core 2.
+---
+
 # Inference
 
diff --git a/docs-gb/user-guide/v2/README.md b/docs-gb/user-guide/v2/README.md
@@ -1,3 +1,7 @@
+---
+description: Open Inference Protocol in Seldon Core 2.
+---
+
 # Open Inference Protocol
 
 This page describes a predict/inference API independent of any\
diff --git a/docs-gb/user-guide/v2/rest/README.md b/docs-gb/user-guide/v2/rest/README.md
@@ -1,2 +1,6 @@
+---
+description: REST in Seldon Core 2.
+---
+
 # REST
 
