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Add Model Deployment overview
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docs/deploying_models.md
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docs/deploying_models.md
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---
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title: Model Deployment
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---
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Model deployment is a critical step in the machine learning lifecycle, enabling trained models to be accessible for
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real-world applications. ClearML provides a comprehensive suite of tools for seamless model deployment, which supports
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features including:
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* Version control
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* Automatic updates
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* Performance monitoring
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ClearML's offerings optimize the deployment process
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while ensuring scalability and security. The solutions include:
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* **Model Deployment UI Applications** (available under the Enterprise Plan) - The UI applications simplify deploying models
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as network services through secure endpoints, providing an interface for managing deployments--no code required.
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See more information about the following applications:
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* [vLLM Deployment](webapp/applications/apps_model_deployment.md)
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* [Embedding Model Deployment](webapp/applications/apps_embed_model_deployment.md)
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* [Llama.cpp Model Deployment](webapp/applications/apps_llama_deployment.md)
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* **Command-line Interface** - `clearml-serving` is a CLI for model deployment and orchestration.
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It supports integration with Kubernetes clusters or custom container-based
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solutions, offering flexibility for diverse infrastructure setups.
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For more information, see [ClearML Serving](clearml_serving/clearml_serving.md).
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## Model Endpoint Monitoring
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All deployed models are displayed in a unified **Model Endpoints** list in the UI. This
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allows users to monitor endpoint activity and manage deployments from a single location.
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For more information, see [Model Endpoints](webapp/webapp_model_endpoints.md).
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@@ -88,7 +88,12 @@ module.exports = {
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]
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},
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{
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"Model Deployment": [
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type: 'category',
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collapsible: true,
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collapsed: true,
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label: 'Model Deployment',
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link: {type: 'doc', id: 'deploying_models'},
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items: [
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{
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type: 'category',
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collapsible: true,
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