ray/doc/source/serve/tutorials/sklearn.md

(serve-sklearn-tutorial)=

Scikit-Learn Tutorial

In this guide, we will train and deploy a simple Scikit-Learn classifier. In particular, we show:

• How to load the model from file system in your Ray Serve definition
• How to parse the JSON request and evaluated in sklearn model

Please see the {doc}../core-apis to learn more general information about Ray Serve.

Ray Serve is framework agnostic. You can use any version of sklearn.

pip install scikit-learn

Let's import Ray Serve and some other helpers.

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We will train a logistic regression with the iris dataset.

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Services are just defined as normal classes with __init__ and __call__ methods. The __call__ method will be invoked per request.

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Now that we've defined our services, let's deploy the model to Ray Serve. We will define a Serve deployment that will be exposed over an HTTP route.

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Let's query it!

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