Description: Anomaly detection models are tools specifically designed to identify unusual or unexpected patterns in datasets. These models are fundamental in data analysis, as they allow organizations to detect anomalous behaviors that may indicate problems, fraud, or system failures. Anomaly detection is based on the premise that most data behaves predictably, and any significant deviation from this behavior may warrant investigation. Multimodal models, in particular, integrate multiple types of data, such as text, images, and signals, to enhance detection accuracy. This is especially relevant in contexts where information comes from various sources and formats, allowing for a more holistic understanding of anomalies. The ability of these models to learn from different data modalities gives them a significant advantage over unidimensional approaches, as they can capture complex relationships and patterns that might otherwise go unnoticed. In a world where the amount of generated data is overwhelming, anomaly detection models have become essential tools for security, service quality, and informed decision-making.
