Export ML Models and Use in the Web App

Machine learning models have become an integral part of many web applications, as they can provide valuable insights and predictions based on data. In this article, we will discuss how to export ML models and use them in a web app using FastAPI and REST APIs.

Exporting ML Models

Before we can use a machine learning model in a web app, we need to export it so that it can be used independently of the training environment. One common way to export an ML model is to save it in a serialized form using libraries like joblib or pickle in Python.

Using FastAPI for Web App

FastAPI is a modern web framework for building APIs with Python that is fast, easy to use, and efficient. We can use FastAPI to create a REST API that serves our ML model predictions to the web app.

Creating REST APIs

With FastAPI, we can easily create REST APIs that expose our ML model predictions. We can define endpoint routes that accept input data, pass it through the ML model, and return the predictions to the client.

Integrating ML Models in Web App

Once we have set up our REST API using FastAPI, we can integrate our ML model in a web app by making API requests to the endpoint routes. The web app can send input data to the API and display the predictions returned by the model.

Conclusion

Exporting ML models and using them in web apps can provide valuable functionality and insights to users. By leveraging FastAPI and REST APIs, we can create a seamless integration between our ML models and web applications, enabling us to deliver powerful predictions and insights to users.