{"id":370,"date":"2022-12-21T00:00:00","date_gmt":"2022-12-21T00:00:00","guid":{"rendered":"https:\/\/tac.debuzzify.com\/?p=370"},"modified":"2023-06-27T04:42:16","modified_gmt":"2023-06-27T04:42:16","slug":"deploy-machine-learning-models-using-fast-api","status":"publish","type":"post","link":"https:\/\/www.the-analytics.club\/deploy-machine-learning-models-using-fast-api\/","title":{"rendered":"How to Deploy Machine Learning Models Using Fast API"},"content":{"rendered":"\n\n\n
One of the biggest challenges for data scientists is to deploy their models. What use if other services or users can’t consume their perfect model?<\/p>\n\n\n\n
But if you know some Python, you can quickly wrap your models around an API endpoint. That’s the focus of this post.<\/p>\n\n\n\n
If you know some Python web technologies<\/a>, you’d know Django and Flask. I’m personally a fan of these frameworks. Yet, for this tutorial, I’m going to use Fast API. And I’d recommend Fast API for production uses also.<\/p>\n\n\n\n We’ll first discuss why Fast API, then deploy a Scikit-learn model<\/a>. Then, we’ll also take a look at how to wrap another ML service with an API endpoint. Finally, we’ll discuss some ways you can secure your endpoints.<\/p>\n\n\n\n Grab your aromatic coffee <\/a>(or tea<\/a>) and get ready…!<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n FastAPI is a popular choice for deploying machine learning models because it is fast, efficient, and easy to use. Here are a few reasons why FastAPI is often preferred over other web frameworks like Flask and Django for deploying machine learning models:<\/p>\n\n\n\n FastAPI is a powerful and easy-to-use web framework well-suited for building and deploying machine learning models in production. While Flask and Django are also popular web frameworks<\/a>, they may not be as well-suited for this specific use case due to their focus on different features and goals.<\/p>\n\n\n\n To deploy a scikit-learn model with FastAPI, you can follow these steps:<\/p>\n\n\n\n 1. You’ll need to install FastAPI and other necessary libraries, such as Scikit-learn and NumPy. You can do this by running the following command:<\/a><\/p>\n\n\n\nWhy is Fast API the best to deploy ML models in production?<\/h2>\n\n\n\n
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Deploy a Scikit-learn model using Fast API.<\/h2>\n\n\n\n