Tracking datasets with DVC in MLOps - Mini Project: Build & Apply

Tracking datasets with DVC

📖 Scenario: You are working on a machine learning project where you need to manage and track your datasets efficiently. Using DVC (Data Version Control) helps you keep track of dataset changes just like code changes in Git.This project will guide you through setting up DVC to track a dataset file in your project folder.

🎯 Goal: Learn how to initialize DVC in a project, add a dataset file to DVC tracking, and verify that DVC is tracking the dataset correctly.

📋 What You'll Learn

Have Git installed and initialized in your project folder

Have DVC installed on your system

Create a dataset file named data.csv with sample data

Initialize DVC in the project folder

Add data.csv to DVC tracking

Check the DVC status to confirm tracking

💡 Why This Matters

🌍 Real World

Data scientists and ML engineers use DVC to track datasets and model files so they can reproduce experiments and collaborate easily.

💼 Career

Knowing how to use DVC is important for roles in machine learning operations (MLOps), data engineering, and data science to manage data versioning professionally.

1

Create a dataset file data.csv

Create a file named data.csv in your project folder with the following exact content:
id,value 1,100 2,200 3,300

MLOps

# Create the file data.csv with the exact content
# Your code here

Need a hint?

You can create the file using a text editor or by running echo -e "id,value\n1,100\n2,200\n3,300" > data.csv in your terminal.

2

Initialize DVC in the project folder

Run the command dvc init in your project folder to initialize DVC tracking.

MLOps

!echo -e "id,value\n1,100\n2,200\n3,300" > data.csv
# Run 'dvc init' command below
# Your code here

Need a hint?

Type dvc init in your terminal inside the project folder to set up DVC.

3

Add data.csv to DVC tracking

Run the command dvc add data.csv to add the dataset file data.csv to DVC tracking.

MLOps

!echo -e "id,value\n1,100\n2,200\n3,300" > data.csv
# Command to initialize DVC
!dvc init
# Run 'dvc add data.csv' command below
# Your code here

Need a hint?

Use dvc add data.csv to tell DVC to track the dataset file.

4

Check DVC status to confirm tracking

Run the command dvc status to check the status of DVC tracking and confirm that data.csv is tracked.

MLOps

!echo -e "id,value\n1,100\n2,200\n3,300" > data.csv
# Command to initialize DVC
!dvc init
# Command to add data.csv to DVC
!dvc add data.csv
# Run 'dvc status' command below
# Your code here

Need a hint?

After adding the file, dvc status should show Data and pipelines are up to date. if tracking is successful.