Q. What is a common challenge faced during model deployment?
A.
Overfitting the model
B.
Data drift
C.
Feature selection
D.
Hyperparameter tuning
Solution
Data drift is a common challenge during model deployment, as the statistical properties of the input data may change over time, affecting model performance.
Q. What is model deployment in the context of machine learning?
A.
Training a model on a dataset
B.
Integrating a model into a production environment
C.
Evaluating model performance
D.
Collecting data for training
Solution
Model deployment refers to the process of integrating a machine learning model into a production environment where it can make predictions on new data.
Correct Answer:
B
— Integrating a model into a production environment
Q. What is the purpose of monitoring a deployed machine learning model?
A.
To ensure the model is still accurate over time
B.
To collect more training data
C.
To improve the model's architecture
D.
To reduce the model's size
Solution
Monitoring a deployed machine learning model is essential to ensure that it maintains accuracy and performance over time as data and conditions change.
Correct Answer:
A
— To ensure the model is still accurate over time
Q. What is the significance of version control in model deployment?
A.
To track changes in the model and its performance
B.
To improve model training speed
C.
To enhance data preprocessing
D.
To reduce model complexity
Solution
Version control is significant in model deployment as it allows teams to track changes in the model and its performance over time, facilitating better management and rollback if necessary.
Correct Answer:
A
— To track changes in the model and its performance
