Description: Model scalability in the context of MLOps refers to the ability of a machine learning model to handle increasing amounts of data or requests without losing performance. This feature is fundamental in environments where data volumes can grow rapidly, such as in e-commerce applications, social networks, or real-time monitoring systems. Scalability can be vertical, where the capacity of a single server is improved, or horizontal, where more servers are added to distribute the load. A scalable model must not only be able to process more data but also adapt to changes in system architecture and new user demands. This implies that the model must be efficient in terms of response time and resource usage, allowing organizations to maintain a high level of service as they grow. Scalability is also related to the ability of a model to be updated or retrained with new data without interrupting service, which is crucial for maintaining the model’s relevance and accuracy over time.
