Pipeline best practices in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a machine learning pipeline?

A machine learning pipeline is a series of steps that process data and train a model in an organized way, like a recipe that ensures each step happens in order.

beginner

Why should you separate data preprocessing and model training in a pipeline?

Separating preprocessing and training helps keep the process clear, makes it easier to fix problems, and ensures the same steps are applied to new data.

intermediate

What is the benefit of using automated pipelines?

Automated pipelines save time, reduce mistakes, and make it easy to repeat experiments or update models with new data.

intermediate

How does version control help in machine learning pipelines?

Version control tracks changes in code and data, so you can go back to earlier versions if something breaks or compare results over time.

intermediate

What is the role of testing in machine learning pipelines?

Testing checks that each step works correctly, which helps catch errors early and keeps the pipeline reliable.

What is the first step in a typical machine learning pipeline?

A Model evaluation

B Model training

C Data preprocessing

D Deployment

Data preprocessing is the first step to clean and prepare data before training a model.

Why is it important to automate machine learning pipelines?

A To reduce human errors and save time

B To make the process slower

C To avoid using data

D To make models less accurate

Automation helps reduce mistakes and speeds up the workflow.

Which practice helps ensure reproducibility in pipelines?

A Version controlling code and data

B Ignoring data versions

C Using random data every time

D Skipping testing

Version control keeps track of changes so results can be repeated.

What should you do if a pipeline step fails?

A Ignore the error and continue

B Fix the error and rerun the pipeline

C Delete the pipeline

D Change the data randomly

Fixing errors and rerunning ensures the pipeline works correctly.

Which of these is NOT a best practice for pipelines?

A Automating workflows

B Clear separation of steps

C Testing each step

D Manual repetitive tasks

Manual repetitive tasks increase errors and waste time.

Explain the key best practices to follow when building a machine learning pipeline.

Think about how to keep the pipeline clear, reliable, and repeatable.

Describe why automation and testing are important in machine learning pipelines.

Consider how these practices improve workflow and results.