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[Preparing the ML Model](https://mpolinowski.github.io/docs/IoT-and-Machine-Learning/AIOps/2023-06-17-scikit-learn-model-deployment/2023-06-17#preparing-the-ml-model)\n    * Pick a SKLearn model that fits your dataset\n    * Preprocess your dataset and do test trainings / hyperparameter tuning\n    * Fit the model to your dataset\n    * Pickle the trained model for deployment\n2. [Preparing the Model API](https://mpolinowski.github.io/docs/IoT-and-Machine-Learning/AIOps/2023-06-17-scikit-learn-model-deployment/2023-06-17#preparing-the-model-api)\n    * Use Flask to prepare a REST API endpoint for your model\n    * Test predictions using HTTP POST requests\n3. [Containerizing the Application](https://mpolinowski.github.io/docs/IoT-and-Machine-Learning/AIOps/2023-06-17-scikit-learn-model-deployment/2023-06-17#containerizing-the-application)\n    * Wrap the Flask application in a Docker image\n4. [React.js Frontend](https://mpolinowski.github.io/docs/IoT-and-Machine-Learning/AIOps/2023-06-17-scikit-learn-model-deployment/2023-06-17#reactjs-frontend)\n    * Write a React.js frontend that can interface with your REST API\n4. [Serve Frontend using the Flask App](https://mpolinowski.github.io/docs/IoT-and-Machine-Learning/AIOps/2023-06-17-scikit-learn-model-deployment/2023-06-17#adding-the-frontend)\n    * Use Vite.js to render the React frontend to static HTML\n    * Add a static route to your Flask app to serve the frontend from inside the Docker image\n\n\n![Serving your SciKit Learn Model as a Prediction API](https://github.com/mpolinowski/sklearn-model-deployment/blob/master/04_react_frontend/SciKit_Learn_Model_API.gif)\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmpolinowski%2Fsklearn-model-deployment","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmpolinowski%2Fsklearn-model-deployment","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmpolinowski%2Fsklearn-model-deployment/lists"}