Azure Data Scientists Associate Practice Exam

Including both init and run functions in a scoring script is vital because they serve different but complementary roles in the model deployment process. The init function is responsible for loading and preparing the model or any required resources once when the service starts. This may include things like loading the model from a file or initializing any necessary libraries or configurations. By executing this function only once, it helps improve performance and efficiency since the model doesn't need to be loaded every time a request is made.

On the other hand, the run function is primarily focused on handling incoming requests, processing input data, invoking the model to generate predictions, and returning results. This separation of responsibilities allows for scalability and optimal resource management. By delineating these functions, developers can ensure that initialization tasks are handled separately and efficiently, while still being able to serve predictions to users in response to their requests.

This structure enhances organization and clarity within the code, making it easier for data scientists and developers to maintain and update the scoring script as needed. Additionally, it aligns with best practices for deploying machine learning models, making it easier to handle different input types and formats.