What does the LAER framework application focus on in MLOps?

The focus of the LAER framework application in MLOps is on defining automation strategies and workflows. This framework emphasizes a structured approach to manage the lifecycle of machine learning models, ensuring that processes are efficient and scalable. By establishing clear automation strategies, organizations can streamline their operations, reduce the manual overhead involved in deploying and maintaining models, and enhance collaboration across teams. This leads to more consistent and reliable outcomes in machine learning projects, supporting continuous integration and delivery practices.

While improving model performance metrics, gathering compliance documentation, and increasing data processing speed are important aspects of MLOps, they are not the primary focus of the LAER framework. Instead, LAER is specifically designed to optimize the processes and workflows that surround the development and deployment of machine learning models, making the overall system more effective in handling real-world applications.