Which of the following stages does not belong in the AI storage process?

In the context of the AI storage process, the correct choice is 'Complication' because it does not refer to a commonly recognized stage within the typical framework of handling AI data. The AI storage process generally involves several key stages that include data ingestion, preparation, and output or inference.

Data ingestion is the initial phase where raw data is collected from various sources and prepared for processing. This is a crucial step because it sets the foundation for the quality and usability of the data in subsequent stages.

Data preparation is the next stage, which involves cleaning, transforming, and organizing the ingested data to make it suitable for analysis. This step is essential for ensuring that the AI models have access to high-quality data that can improve their accuracy and performance.

Finally, the output or inference phase represents the point where the trained AI model makes predictions or draws conclusions based on the prepared data. This stage demonstrates the application of the data throughout the process, delivering insights or results that can be utilized for decision-making.

In contrast, 'Complication' does not fit into this logical sequence or the common terminology used in AI data management, making it the stage that does not belong in the AI storage process.