Monitoring model performance in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main goal of monitoring model performance?

To track how well a machine learning model works over time and detect any drop in accuracy or unexpected behavior.

beginner

Name two common metrics used to monitor classification model performance.

Accuracy and F1-score are common metrics to check if the model predicts correctly and balances precision and recall.

intermediate

What is data drift in model monitoring?

Data drift happens when the input data changes over time, causing the model to perform worse because it sees different patterns than it was trained on.

beginner

Why is alerting important in monitoring model performance?

Alerting notifies the team quickly when the model’s performance drops, so they can fix issues before users are affected.

intermediate

What role does logging play in monitoring machine learning models?

Logging records model inputs, outputs, and errors to help understand model behavior and diagnose problems.

Which metric is best to monitor for a regression model?

A Mean Squared Error (MSE)

B Accuracy

C F1-score

D Confusion Matrix

Mean Squared Error measures the average squared difference between predicted and actual values, suitable for regression.

What does data drift indicate in model monitoring?

A Model code errors

B Hardware failure

C Change in input data distribution over time

D User interface bugs

Data drift means the input data changes, which can reduce model accuracy.

Why should you monitor model latency?

A To measure CPU usage

B To check model accuracy

C To track data storage size

D To ensure predictions are made quickly enough for users

Latency monitoring ensures the model responds fast enough for practical use.

What is a common tool used for monitoring machine learning models?

A Photoshop

B Prometheus

C Excel

D WordPress

Prometheus is widely used for monitoring metrics including those from ML models.

What action should be taken if model performance drops significantly?

A Investigate and retrain the model if needed

B Ignore it

C Delete the model

D Restart the server

A performance drop means the model may need retraining or fixing.

Explain why monitoring model performance is important in real-world applications.

Think about what happens if a model starts making wrong predictions unnoticed.

Describe common metrics and tools used to monitor machine learning models.

Consider both how we measure performance and how we track it.