Data pipelines with DVC in MLOps - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is DVC in the context of data pipelines?

DVC (Data Version Control) is a tool that helps manage data, models, and experiments in machine learning projects. It tracks data changes and automates pipelines like code version control.

beginner

How does DVC help in building data pipelines?

DVC lets you define stages in a pipeline with commands and dependencies. It tracks inputs and outputs, so when data or code changes, only necessary steps rerun, saving time and ensuring reproducibility.

intermediate

What is the purpose of the dvc.yaml file?

The dvc.yaml file describes the pipeline stages, their commands, inputs, and outputs. It acts like a recipe that DVC uses to run and reproduce the pipeline steps.

beginner

How do you run a DVC pipeline after defining it?

You run the command dvc repro. This tells DVC to check for changes and rerun only the pipeline stages that need updating.

beginner

Why is versioning data important in machine learning projects?

Versioning data helps track changes, reproduce results, and collaborate safely. It prevents confusion about which data or model version was used for experiments.

What command initializes a new DVC project?

A dvc new

B dvc start

C dvc create

D dvc init

The command dvc init sets up DVC in your project folder.

Which file stores the pipeline stages in DVC?

A dvc.yaml

B pipeline.json

C dvc.lock

D config.yml

The dvc.yaml file defines pipeline stages, commands, inputs, and outputs.

What does the command dvc repro do?

A Removes old data files

B Uploads data to cloud storage

C Runs the pipeline stages that need updating

D Initializes a new pipeline

dvc repro reruns pipeline stages if inputs or code changed.

Why should data be versioned in ML projects?

A To delete old data automatically

B To track changes and reproduce experiments

C To speed up model training

D To encrypt data for security

Versioning data helps keep track of changes and ensures reproducibility.

Which of these is NOT a DVC pipeline component?

A Containers

B Commands

C Stages

D Inputs/Outputs

DVC pipelines use stages, commands, inputs, and outputs, but not containers.

Explain how DVC helps automate and manage data pipelines in machine learning projects.

Think about how DVC tracks inputs, outputs, and commands to run pipelines efficiently.

Describe the role of the dvc.yaml and dvc.lock files in a DVC pipeline.

One file is like a recipe, the other locks the exact ingredients used.