Description: Model integration in the context of MLOps refers to the process of combining machine learning models with other systems or applications to optimize their performance and facilitate their deployment in production environments. This process involves not only the incorporation of the model itself but also the adaptation of the necessary technological infrastructure and workflows for its effective operation. Model integration is crucial to ensure that machine learning models can interact with databases, APIs, and other applications, allowing the results generated by the models to be effectively used in business decision-making. Furthermore, this integration must consider aspects such as scalability, security, and real-time performance monitoring of the model. The ability to efficiently integrate models is an essential component of MLOps, which aims to bridge the gap between model development and production operation, ensuring that models are not only accurate but also useful and accessible to end users.
