Data drift detection basics in MLOps - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is data drift in machine learning?

Data drift happens when the data used by a machine learning model changes over time, making the model less accurate because it sees different data than it was trained on.

beginner

Why is detecting data drift important?

Detecting data drift helps keep machine learning models accurate by alerting us when the data changes, so we can update or retrain the model.

intermediate

Name one common method to detect data drift.

One common method is to compare statistical properties like mean or distribution of new data against the training data using tests like the Kolmogorov-Smirnov test.

intermediate

What role does a monitoring system play in data drift detection?

A monitoring system automatically checks incoming data for changes and alerts the team if data drift is detected, enabling quick action to maintain model performance.

beginner

How can you respond when data drift is detected?

You can retrain the model with new data, adjust the model, or investigate if the data change is expected or a problem.

What does data drift affect in a machine learning model?

A Model deployment time

B Model training speed

C Model size

D Model accuracy

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

Which statistical test is commonly used to detect data drift?

A Chi-square test for independence

B T-test for means

C Kolmogorov-Smirnov test

D ANOVA

The Kolmogorov-Smirnov test compares distributions to detect drift.

What should you do first when data drift is detected?

A Investigate the cause of the drift

B Ignore it if the model still works

C Retrain the model immediately

D Delete the old data

Investigating the cause helps decide the best response.

Data drift monitoring is usually done how?

A Using automated monitoring tools

B Manually checking data daily

C Only during model training

D By checking model code

Automated tools continuously monitor data for drift.

Which of these is NOT a sign of data drift?

A Change in data distribution

B Model training time decreases

C Model accuracy drops

D New data has unexpected values

Model training time is unrelated to data drift.

Explain what data drift is and why it matters for machine learning models.

Think about how changing data affects a model's predictions.

Describe common ways to detect data drift and how to respond when it happens.

Consider tools and steps after noticing data changes.