59 lines
3.4 KiB
Markdown
59 lines
3.4 KiB
Markdown
---
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layout: default
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title: Integrating ML models
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nav_order: 15
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has_children: true
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---
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# Integrating ML models
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OpenSearch offers support for machine learning (ML) models that you can use in conjunction with k-NN search to retrieve semantically similar documents. This semantic search capability improves search relevance for your applications.
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Before you get started, you'll need to [set up]({{site.url}}{{site.baseurl}}/quickstart/) and [secure]({{site.url}}{{site.baseurl}}/security/index/) your cluster.
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{: .tip}
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## Choosing a model
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To integrate an ML model into your search workflow, choose one of the following options:
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1. **Local model**: Upload a model to the OpenSearch cluster and use it locally. This option allows you to serve the model in your OpenSearch cluster but may require significant system resources.
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1. **Pretrained model provided by OpenSearch**: This option requires minimal setup and avoids the time and effort required to train a custom model.
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For a list of supported models and information about using a pretrained model provided by OpenSearch, see [Pretrained models]({{site.url}}{{site.baseurl}}/ml-commons-plugin/pretrained-models/).
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1. **Custom model**: This option offers customization for your specific use case.
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For information about uploading your model, see [Using ML models within OpenSearch]({{site.url}}{{site.baseurl}}/ml-commons-plugin/using-ml-models/).
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1. **Externally hosted model**: This option allows you to connect to a model hosted on a third-party platform. It requires more setup but allows the use of models that are already hosted on a service other than OpenSearch.
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To connect to an externally hosted model, you need to set up a connector:
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- For a walkthrough with detailed steps, see [Connecting to externally hosted models]({{site.url}}{{site.baseurl}}/ml-commons-plugin/remote-models/index/).
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- For more information about supported connectors, see [Connectors]({{site.url}}{{site.baseurl}}/ml-commons-plugin/remote-models/connectors/).
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- For information about creating your own connector, see [Connector blueprints]({{site.url}}{{site.baseurl}}/ml-commons-plugin/remote-models/blueprints/).
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In OpenSearch version 2.9 and later, you can integrate local and external models simultaneously within a single cluster.
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{: .note}
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## Tutorial
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For a step-by-step tutorial, see [Neural search tutorial]({{site.url}}{{site.baseurl}}/search-plugins/neural-search-tutorial/).
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## Using a model
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You can use an ML model in one of the following ways:
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- [Make predictions](#making-predictions).
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- [Use a model for search](#using-a-model-for-search).
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### Making predictions
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[Models trained]({{site.url}}{{site.baseurl}}//ml-commons-plugin/api/train-predict/train/) through the ML Commons plugin support model-based algorithms, such as k-means. After you've trained a model to your precision requirements, use the model to [make predictions]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/train-predict/predict/).
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If you don't want to use a model, you can use the [Train and Predict API]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/train-predict/train-and-predict/) to test your model without having to evaluate the model's performance.
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### Using a model for search
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OpenSearch supports multiple search methods that integrate with ML models. For more information, see [Search methods]({{site.url}}{{site.baseurl}}/search-plugins/index/#search-methods). |