Which process involves converting unstructured data into vector embeddings?

The process of converting unstructured data into vector embeddings is known as vectorization. This involves transforming raw data—such as text, images, or audio—into a format that allows it to be represented in a numerical way suitable for machine learning algorithms and AI applications. Vectorization typically employs techniques such as word embeddings or feature extraction to represent data points as vectors in a high-dimensional space.

This process is critical because it allows algorithms to analyze and understand the relationships between different pieces of data in a meaningful way. By creating vector embeddings, unstructured data can be utilized for various downstream tasks such as classification, clustering, and search operations in AI applications.

Normalization, tokenization, and segmentation serve different purposes in data processing. Normalization involves adjusting values measured on different scales to a common scale, which is not directly related to the creation of vector embeddings. Tokenization is the process of splitting text into individual words or tokens, which can be a precursor to vectorization but is not the conversion itself. Segmentation refers to dividing data into segments or sections but does not involve the transformation into vector embeddings.