How can you use a trained machine learning model in your application?

Publishing a trained machine learning model as a web service using Azure Machine Learning is a standard practice in integrating machine learning models into applications. This approach allows you to expose the model's predictive capabilities over the internet, making it accessible through RESTful API calls. By doing this, your application can send requests to the model to obtain predictions without needing to embed the entire model directly into the application code.

This method provides several advantages, including scalability, ease of updates, and the ability to manage the model separately from the application. When a new version of the model is created or retrained, you can simply update the web service without needing to redeploy your entire application. Moreover, this approach enhances collaboration, allowing different teams to work independently on the application and the model.

The other options may have their purposes but do not effectively encapsulate the best practices surrounding model integration into applications. For instance, while exporting a model as a cognitive service might seem appealing, it specifically pertains to Azure’s pre-built AI models rather than custom models that a user has trained with Azure Machine Learning. Using the Azure Machine Learning designer is indeed a useful tool for building models, but it doesn’t directly speak to the integration process. Integrating the model directly into application code could limit