You recently joined a machine learning team that will soon release a new project. As a lead on the project, you are asked to determine the production…

Monitoring model performance is an essential part of production readiness, as it allows the team to

detect and address any issues that may arise after deployment, such as data drift, model

degradation, or errors.

Other Options:

A . Ensuring that training is reproducible is important for model development, but not necessarily for

production readiness. Reproducibility helps the team to track and compare different experiments,

but it does not guarantee that the model will perform well in production.

B . Ensuring that all hyperparameters are tuned is also important for model development, but not

sufficient for production readiness. Hyperparameter tuning helps the team to find the optimal

configuration for the model, but it does not account for the dynamic and changing nature of the

production environment.

D . Ensuring that feature expectations are captured in the schema is a part of testing features and

data, which the team has already done. The schema defines the expected format, type, and range of

the features, and helps the team to validate and preprocess the data.