How To Integrate Machine Learning Models To Your Web Applications

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Integrating a trained machine learning model into a web application can be a complex process, but with the right tools and approach, it can be done relatively easily.

First, you’ll need to determine which framework or platform you’ll be using to build your web application. Popular choices include Flask or Django for Python, and Express.js for JavaScript.

Once you’ve chosen your framework, you’ll need to export your trained model in a format that can be loaded by your web application. One popular format for this is the ONNX format, which can be used with a variety of programming languages and platforms.

Here is an example of how to export a trained model in the ONNX format using the Python library PyTorch:

import torch
import onnx

# load your trained model
model = torch.load("trained_model.pt")

# export the model in the ONNX format
onnx_model = onnx.export(model, input_data, "trained_model.onnx")

Once your model is in the ONNX format, you can load it into your web application using a library such as ONNX.js, which allows you to run ONNX models in the browser or on Node.js.

Here is an example of how to load an ONNX model in JavaScript using ONNX.js:

const onnx = require("onnxjs")…