Data validation in CI pipeline in MLOps - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of data validation in a CI pipeline?

Data validation in a CI pipeline ensures that the data used for machine learning is clean, correct, and meets quality standards before training or deployment.

beginner

Name a common tool or library used for data validation in ML pipelines.

Great Expectations is a popular open-source tool used to create, manage, and run data validation tests in ML pipelines.

beginner

What happens if data validation fails during a CI pipeline run?

If data validation fails, the CI pipeline stops the process to prevent bad data from moving forward, protecting model quality and system reliability.

intermediate

Why is automating data validation important in CI pipelines?

Automation saves time, reduces human error, and ensures consistent checks every time new data is added or changed.

beginner

Give an example of a simple data validation check in a CI pipeline.

Checking if a dataset has any missing values or if all required columns exist before training starts.

What is the first step in data validation within a CI pipeline?

A Check data quality and schema

B Train the model

C Deploy the model

D Run unit tests on code

Data validation starts by checking the data quality and schema before any model training or deployment.

Which tool is commonly used for data validation in ML pipelines?

A Docker

B Great Expectations

C Kubernetes

D Jenkins

Great Expectations is designed specifically for data validation in ML workflows.

What should happen if data validation fails in a CI pipeline?

A Pipeline stops and reports error

B Pipeline continues to deploy model

C Pipeline ignores the error

D Pipeline deletes the data

Failing validation should stop the pipeline to prevent bad data from affecting the model.

Why automate data validation in CI pipelines?

A To slow down deployment

B To make data dirty

C To skip testing

D To save time and avoid mistakes

Automation ensures fast, consistent, and error-free validation.

Which of these is a simple data validation check?

A Train a deep learning model

B Deploy to production

C Check for missing values

D Write documentation

Checking for missing values is a basic but important data validation step.

Explain why data validation is critical in a CI pipeline for machine learning projects.

Think about what happens if bad data reaches the model.

Describe how you would implement a simple data validation step in a CI pipeline.

Focus on checks before model training.