> ## Documentation Index
> Fetch the complete documentation index at: https://doc.lucidworks.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Seldon

export const LwTemplate = ({title = "Key questions to get you started", icon = "sparkles", cta = "Powered by Agent Studio", linkHref = "https://lucidworks.com/demo/?utm_source=docs&utm_medium=referral&utm_campaign=docs_cta_ai"}) => {
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    return () => clearTimeout(timer);
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  return <div className="lw-template-container">
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        {isLoaded && <span dangerouslySetInnerHTML={{
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        <Link href={linkHref} className="agent-studio-link text-left text-gray-600 gap-2 dark:text-gray-400 text-sm font-medium flex flex-row items-center hover:text-primary dark:hover:text-primary-light group-hover:text-primary group-hover:dark:text-primary-light">Powered by Lucidworks Agent Studio</Link>
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[localhost link]: http://localhost:3000/docs/lucidworks-search/09-developer-documentation/config-specs/query-pipeline-stages/seldon

[mintlify link]: https://doc.lucidworks.com/docs/lucidworks-search/09-developer-documentation/config-specs/query-pipeline-stages/seldon

[old doc.lw link]: https://archivedoc.lucidworks.com/managed-fusion/5.9/879fdz/seldon

Seldon is an open-source platform used to deploy, scale, and monitor machine learning (ML) models in Kubernetes environments. It is integrated into Lucidworks Search to enable model-serving capabilities for inference workflows. For more information, see [Seldon Core documentation](https://docs.seldon.io/projects/seldon-core) for more details.

<LwTemplate />

## Seldon models and endpoints

Lucidworks Search Seldon models are trained on different lengths of text such as short and long sentences or phrases, as well as varied collections for B2B, B2C, and knowledge management entities.

Models are exposed via a unique endpoint, and are used in a pipeline stage or external application. Endpoints can be secured using Istio or other methods through the Lucidworks Search gateway.

Supported models include general purpose sentiment prediction, biomedical text collections, and large corpuses in a multiple languages. Based on the model, Seldon supports input formats such as `ndarray`, `tensor`, and `json`. Outputs are in a structured JSON format, typically wrapped in metadata, which supports monitoring and logging functions.

## Seldon configuration and management

This section provides links to more detailed information to configure and manage Seldon deployments for Lucidworks Search users.

* [Create Seldon Core Model deployment](/docs/lucidworks-search/09-developer-documentation/config-specs/jobs/create-seldon-core-model-deploy) in a Lucidworks Search cluster
* [Update Seldon Model deployment](#update-a-seldon-model-deployment)
* [Delete Seldon Core Model deployment](/docs/lucidworks-search/09-developer-documentation/config-specs/jobs/delete-seldon-core-model-deployment)
* [Configure Ray/Seldon vector search](/docs/lucidworks-search/11-vector-search/overview#configure-ray-seldon-vector-search) in Neural Hybrid Search
* [Ray/Seldon Vectorize Query stage](/docs/lucidworks-search/09-developer-documentation/config-specs/query-pipeline-stages/ray-seldon-vectorize-query-stage) to generate a vector based on a query string
* [Ray/Seldon Vectorize Field stage](/docs/lucidworks-search/09-developer-documentation/config-specs/index-pipeline-stages/ray-seldon-vectorize-field-stage) to invoke a machine learning model that encodes a string field to a vector representation

### Update a Seldon model deployment

Seldon does not support in-place updates to certain deployment properties, for example, protocol or runtime class. To apply significant updates:

1. Deploy a new version of the model using a different deployment name.
2. Execute test requests to validate the new endpoint.
3. Update the Fusion pipeline with the new endpoint information.
4. Delete the previous Seldon deployment when the migration is complete.