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

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

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

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

Hint

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

Practice

1. What does the dvc add command do when tracking datasets?

easy

A. It deletes the dataset from the local machine.

B. It uploads the dataset directly to GitHub.

C. It converts the dataset into a database format.

D. It creates a pointer file to track the dataset without storing the data in Git.

Solution

1. Step 1: Understand dvc add purpose

   The dvc add command creates a small pointer file that represents the dataset, instead of storing the full data in Git.

2. Step 2: Recognize data management with DVC

   This pointer file allows Git to track dataset versions without handling large files directly.

3. Final Answer:

   It creates a pointer file to track the dataset without storing the data in Git. -> Option D

4. Quick Check:

   dvc add creates pointer file [OK]

Hint: Remember: DVC tracks data with pointer files, not full data [OK]

Common Mistakes:

• Thinking dvc add uploads data to GitHub
• Confusing dvc add with deleting files
• Assuming data is converted or changed format

2. Which of the following is the correct syntax to track a dataset file named data.csv using DVC?

easy

A. dvc track data.csv

B. dvc add data.csv

C. dvc push data.csv

D. dvc commit data.csv

Solution

1. Step 1: Identify the correct DVC command for tracking

   The command to start tracking a dataset file is dvc add followed by the filename.

2. Step 2: Confirm syntax correctness

   Among the options, only dvc add data.csv correctly adds the file to DVC tracking.

3. Final Answer:

   dvc add data.csv -> Option B

4. Quick Check:

   Use dvc add filename to track data [OK]

Hint: Use dvc add to start tracking files [OK]

Common Mistakes:

• Using dvc track which is not a valid command
• Confusing dvc push with adding files
• Trying dvc commit which is a Git command

3. After running dvc add data.csv, what is the expected output or change in the project directory?

medium

A. A new file named data.csv.dvc is created and data.csv remains in the directory.

B. A new file named data.csv.dvc is created and data.csv is removed.

C. The data.csv file is uploaded to GitHub automatically.

D. The data.csv file is converted to a binary format.

Solution

1. Step 1: Understand dvc add effects on files

   Running dvc add creates a pointer file with extension .dvc that tracks the dataset, but does not delete the original data file.

2. Step 2: Confirm directory state after command

   The original data.csv remains, and a new data.csv.dvc file appears to track it.

3. Final Answer:

   A new file named data.csv.dvc is created and data.csv remains in the directory. -> Option A

4. Quick Check:

   dvc add creates pointer file, keeps data [OK]

Hint: Look for .dvc pointer file; data file stays [OK]

Common Mistakes:

• Assuming data file is deleted after dvc add
• Thinking data is uploaded automatically to GitHub
• Believing data file is converted or changed format

4. You ran dvc add dataset.csv but forgot to commit the generated dataset.csv.dvc file to Git. What problem might occur?

medium

A. Git will track the dataset file directly, causing large repository size.

B. DVC will stop tracking the dataset automatically.

C. The dataset pointer file won't be versioned, causing sync issues between code and data.

D. The dataset file will be deleted from the local machine.

Solution

1. Step 1: Understand the role of the pointer file in Git

   The .dvc pointer file must be committed to Git to keep track of dataset versions alongside code.

2. Step 2: Identify consequences of not committing pointer file

   If the pointer file is not committed, Git won't know about dataset changes, causing mismatch between code and data versions.

3. Final Answer:

   The dataset pointer file won't be versioned, causing sync issues between code and data. -> Option C

4. Quick Check:

   Commit pointer files to Git to sync data and code [OK]

Hint: Always commit .dvc files to Git after adding data [OK]

Common Mistakes:

• Assuming DVC stops tracking automatically
• Thinking dataset file is deleted if not committed
• Believing Git tracks large data files directly

5. You have a dataset folder named images/ with many files. You want to track it with DVC and ensure the dataset version is saved and shared with your team. Which sequence of commands is correct?

hard

A. dvc add images/; git add images.dvc; git commit -m 'Track images dataset'; git push; dvc push

B. git add images/; dvc add images/; git commit -m 'Track images dataset'; dvc push

C. dvc add images/; git add images.dvc; git commit -m 'Track images dataset'; git push

D. dvc add images/; git add images.dvc; git commit -m 'Track images dataset'; dvc push

Solution

1. Step 1: Add the dataset folder with DVC

   Use dvc add images/ to create the pointer file images.dvc tracking the folder.

2. Step 2: Commit the pointer file to Git

   Run git add images.dvc and git commit to version control the pointer file.

3. Step 3: Push Git changes and dataset to remote storage

   First push Git commits with git push, then push dataset files to remote storage with dvc push.

4. Final Answer:

   dvc add images/; git add images.dvc; git commit -m 'Track images dataset'; git push; dvc push -> Option A

5. Quick Check:

   Push Git first, then DVC data to share [OK]

Hint: Push Git commits before dvc push to sync data [OK]

Common Mistakes:

• Pushing DVC data before Git commits
• Adding dataset files directly to Git
• Forgetting to push Git commits before dvc push