Prediction distribution monitoring in MLOps - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is prediction distribution monitoring in MLOps?

It is the process of tracking the patterns and changes in the predictions made by a machine learning model over time to detect shifts or anomalies.

beginner

Why is monitoring prediction distribution important?

Because changes in prediction patterns can indicate model drift, data issues, or changes in the environment that affect model accuracy.

intermediate

Name a common method to detect changes in prediction distribution.

Statistical tests like the Kolmogorov-Smirnov test or monitoring summary statistics such as mean and variance over time.

intermediate

What is model drift and how does it relate to prediction distribution monitoring?

Model drift happens when the model's predictions change because the data or environment changes. Prediction distribution monitoring helps detect this drift early.

beginner

Give an example of a tool or platform that supports prediction distribution monitoring.

Tools like Evidently AI, WhyLabs, or custom dashboards using Prometheus and Grafana can monitor prediction distributions.

What does prediction distribution monitoring primarily track?

A Changes in the model's output predictions over time

B The speed of model training

C The size of the training dataset

D The number of model parameters

Prediction distribution monitoring focuses on tracking how the model's predictions change over time.

Which of the following indicates a potential problem detected by prediction distribution monitoring?

A Stable prediction patterns

B Sudden shift in prediction values

C Increased training speed

D More features added to the model

A sudden shift in prediction values can indicate model drift or data issues.

Which statistical test is commonly used to compare prediction distributions over time?

A T-test for means

B Chi-square test for independence

C Kolmogorov-Smirnov test

D ANOVA test

The Kolmogorov-Smirnov test compares two distributions to detect differences.

What is model drift?

A When the model is deployed to production

B When the model trains faster

C When the model size increases

D When the model's predictions become less accurate due to changes in data or environment

Model drift means the model's performance degrades because the data or environment changes.

Which tool can be used for prediction distribution monitoring?

A Evidently AI

B Docker

C Git

D Jenkins

Evidently AI is designed for monitoring ML model predictions and data.

Explain what prediction distribution monitoring is and why it matters in MLOps.

Think about how changes in model outputs over time can affect performance.

Describe methods or tools you can use to monitor prediction distributions effectively.

Consider both manual and automated approaches.