Uncertain Fascination

Hallie has always been fascinated by artificial intelligence and is eager to embark on her career in the field.

However, she's uncertain whether to focus on research or dive into a more industry-oriented role.

To help her make an informed decision, we will outline some key differences between Machine Learning in a research setting and Machine Learning in a production setting.

Select every statement that correctly highlights the differences between Machine Learning in a research environment versus Machine Learning in production.

The design priority in a research environment usually leans towards higher performance—either the highest accuracy or other relevant metrics. Production environments place more emphasis on costs, scalability, and explainability.

In a research environment, most work is centered around the initial training and validation of the model. In production, there is a significant focus on monitoring and maintaining models.

The data used in a research environment is typically static, while the data used in a production setting is dynamic and constantly evolving.

Both research and production environments are concerned with fairness, but the implications of fairness in a production environment are often more critical due to real-world consequences.