Which of the following is a component of data preprocessing?

Data preprocessing is a crucial step in the machine learning and AI workflow that involves preparing the raw data for further analysis and model training. This stage includes various tasks that enhance the quality and usability of the data, ensuring that it is suitable for modeling purposes.

Data normalization and cleaning are key processes in preprocessing. Normalization involves scaling the data to have consistent units or ranges, which helps improve the model's performance by ensuring that no single feature dominates due to its magnitude. Cleaning, on the other hand, entails removing errors, filling in missing values, and addressing inconsistencies in the dataset. These steps are vital for creating high-quality input for machine learning algorithms, thereby enhancing the accuracy and reliability of the models trained on this data.

In contrast, the other options represent processes that occur at different stages of the AI development lifecycle. Model evaluation procedures focus on assessing the performance of trained models against validation or test datasets, ensuring they generalize well to unseen data. Designing neural network architecture pertains to defining the structure and layers of the model, which is part of the model development phase rather than preprocessing. Lastly, implementation of user interfaces relates to creating the front-end components of applications that utilize AI models, which is also a subsequent step in the AI development process. Thus,