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 Key Concepts 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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