How to integrate WeWeb with a custom machine learning model for predictive analytics?

Step 1: Develop and Train Your Machine Learning Model

Before integrating WeWeb with a custom machine learning model for predictive analytics, you need to have a developed and trained model. There are several programming languages and libraries you can use for this task, such as Python with Scikit-learn or TensorFlow. Once you have prepared the model, validate it against a sample dataset.

Step 2: Convert Your Machine Learning Model into a Web Service

The next step is to convert your machine learning model into a web service, which allows for interfacing with WeWeb. An effective method of accomplishing this is by using Flask, a micro web framework written in Python. Flask allows you to serve your model via HTTP requests. You will need to define routes where the requests can be sent and responses received.

Step 3: Deploy the Web Service

Once ready, deploy the web service, with your machine learning model, on a server. You can use services such as AWS, Google Cloud, or Heroku. After deployment, test the service to ensure it's up and running as expected.

Step 4: Create a WeWeb Website

Now, head over to WeWeb and start building your website. WeWeb's interface is very intuitive and easy to navigate. Choose a template that suits your needs and start developing your pages.

Step 5: Use WeWeb's CORS proxy to Integrate Your Model

WeWeb provides a serverless function feature that allows you to call an API directly from your pages. One of these serverless functions is a CORS proxy that you can use to bridge the gap between your machine learning model web service and your WeWeb website.

In general, to use the serverless features, go to the "Serverless" tab on the WeWeb platform, and choose the CORS Proxy function. Input the URL of your deployed machine learning model, and define the headers, method and body as necessary.

Step 6: Define the Interaction Between WeWeb and the Model

Now that you have a working connection between your WeWeb site and your Machine learning model web service through the CORS proxy, you'll have to define how the WeWeb interacts with the model. This depends entirely on your particular use case.

For instance, your model might be designed to predict customer behavior based on given inputs. The WeWeb site, therefore, would need to gather those inputs (either from user input or from stored data), send them off to the model, and then display the results in a meaningful way.

Use WeWeb's visual programming tool to define these interactions.

Step 7: Test the Integration

Finally, test the integration thoroughly. Carry out predictive analytics using a sample dataset, and compare the results to your expectations. Make any necessary adjustments to improve accuracy and reliability.

Step 8: Publish Your WeWeb Site

Once all the integration testing has been carried out successfully, publish your WeWeb site. Your website with predictive analytics powered by a custom machine learning model is now live and ready for use.