👉 Output engineering is a crucial phase in the machine learning pipeline that focuses on transforming the model's predictions into actionable and understandable outputs for end-users. This process involves refining, customizing, and optimizing the model's outputs to meet specific application requirements, ensuring they are accurate, relevant, and presented in a user-friendly manner. It includes tasks such as data formatting, decision mapping, error handling, and integration with external systems or interfaces. Effective output engineering bridges the gap between complex model outputs and practical, real-world applications, enhancing user experience and operational efficiency.
