AI Engineering Degree Practice Exam 2025 - Free AI Engineering Practice Questions and Study Guide

The primary aim of model deployment is to make a trained model available for real-world applications. This process transforms a theoretical model, developed during the training phase, into a practical tool that can be utilized in everyday situations. Deployment ensures that the insights and predictions generated by the model can be integrated into applications, services, or products that provide value to users.

In this context, the deployment can involve creating APIs, integrating the model into software applications, or providing it through cloud services. The goal is to allow end-users to benefit from the predictions and analyses generated by the model, making it an essential step in the workflow of machine learning or artificial intelligence projects.

The other options focus on activities that are not directly related to the operational use of a model in practical scenarios. Improving data preprocessing techniques pertains more to refining initial steps of the data pipeline, while publishing research findings and conducting further academic research relate to knowledge dissemination and exploration rather than applying a model in the field.