Explanation:
Correct Answer: C. Making the model available for use in a production environment, ensuring it is integrated into the CRM system, supports high availability and scalability, and complies with data privacy regulations.
Explanation: Model deployment is the process of making a trained machine learning model available for use in a production environment. In this scenario, the primary focus is on integrating the model into the company's CRM system to provide real-time predictions. This involves ensuring the model can handle high volumes of requests (scalability), is always available (high availability), and adheres to data privacy laws (compliance). These are critical aspects of deploying a model in a real-world, production setting where it can deliver value to the business.
Incorrect Options:
Ultimate access to all questions.
No comments yet.
In the context of ML model development, what is the primary focus of model deployment? Consider the following scenario: A company has developed a machine learning model to predict customer churn. The model has been trained and validated with historical data. The company now wants to integrate this model into their customer relationship management (CRM) system to provide real-time predictions to their sales team. The integration must support high availability and scalability to handle thousands of predictions per second, and the solution must comply with data privacy regulations. Which of the following best describes the primary focus of model deployment in this scenario? Choose one correct option.
A
Model training, to further improve the model's accuracy before deployment.
B
Creating a model prototype, to test the model's performance in a controlled environment.
C
Making the model available for use in a production environment, ensuring it is integrated into the CRM system, supports high availability and scalability, and complies with data privacy regulations.
D
Gathering more data, to enhance the model's predictive capabilities post-deployment.