Why data versioning is harder than code versioning in MLOps - See It in Action

Why Data Versioning is Harder Than Code Versioning

📖 Scenario: You are working as a machine learning engineer. You want to understand why managing different versions of data is more difficult than managing versions of code. This helps you plan better for your projects.

🎯 Goal: Build a simple Python program that shows a dataset and tracks changes to it step-by-step, illustrating why data versioning is harder than code versioning.

📋 What You'll Learn

Create a dictionary called dataset with exact entries for three data points

Create a variable called version to track the dataset version number

Update the dataset by changing one data point to simulate a new version

Print the dataset and version to show the current state

💡 Why This Matters

🌍 Real World

In real machine learning projects, datasets change often and can be very large. Tracking these changes carefully is important to reproduce results and debug models.

💼 Career

Understanding data versioning challenges helps you work better with data engineers and ML engineers, improving collaboration and project reliability.

1

Create the initial dataset

Create a dictionary called dataset with these exact entries: 'user1': 25, 'user2': 30, 'user3': 22

MLOps

# Create the initial dataset dictionary
# Your code here

Hint

Use curly braces {} to create a dictionary with keys and values.

2

Add a version number variable

Create a variable called version and set it to 1 to track the dataset version

MLOps

dataset = {'user1': 25, 'user2': 30, 'user3': 22}
# Create a version variable and set it to 1
# Your code here

Hint

Just assign the number 1 to the variable version.

3

Update the dataset to simulate a new version

Change the value for 'user2' in dataset to 31 and increase version by 1

MLOps

dataset = {'user1': 25, 'user2': 30, 'user3': 22}
version = 1
# Update 'user2' age to 31 and increment version
# Your code here

Hint

Use dataset['user2'] = 31 to update the value and version += 1 to increase the version.

4

Print the current dataset and version

Write two print statements: one to display dataset and one to display version

MLOps

dataset = {'user1': 25, 'user2': 30, 'user3': 22}
version = 1
dataset['user2'] = 31
version += 1
# Print dataset and version
# Your code here

Hint

Use print(dataset) and print(version) to show the current data and version.

Practice

1.

Why is data versioning generally harder than code versioning?

easy

A. Because code does not need to be tracked for changes.

B. Because code is written in many different programming languages.

C. Because data files are usually much larger and change more frequently than code files.

D. Because data is always stored in databases, unlike code.

Solution

1. Step 1: Understand size and frequency differences

   Data files tend to be much larger and updated more often than code files, making tracking harder.

2. Step 2: Compare code and data versioning challenges

   Code changes are usually smaller and easier to manage with tools like Git, unlike large, frequently changing data.

3. Final Answer:

   Because data files are usually much larger and change more frequently than code files. -> Option C

4. Quick Check:

   Data size and change frequency = D [OK]

Hint: Remember: bigger and frequent changes make data versioning tough [OK]

Common Mistakes:

• Thinking code is harder because of multiple languages
• Assuming data is always in databases
• Believing code doesn't need versioning

2.

Which of the following is a correct statement about data versioning tools?

Choose the correct syntax to initialize a data versioning repository using dvc command line.

easy

A. git dvc init

B. dvc init

C. init dvc

D. dvc start

Solution

1. Step 1: Recall dvc initialization command

   The correct command to start a data versioning repo with DVC is dvc init.

2. Step 2: Eliminate incorrect syntax

   Commands like git dvc init, init dvc, and dvc start are invalid or do not exist.

3. Final Answer:

   dvc init -> Option B

4. Quick Check:

   DVC init command = A [OK]

Hint: Use simple dvc init to start data versioning [OK]

Common Mistakes:

• Adding git before dvc command
• Reversing command words
• Using non-existent commands like dvc start

3.

Consider this simplified code snippet using DVC commands:

dvc add data.csv
git add data.csv.dvc
git commit -m "Add data version"
dvc push

What is the main purpose of the dvc add data.csv command here?

medium

A. It tracks the data file data.csv in DVC and creates a pointer file.

B. It uploads data.csv to the remote storage immediately.

C. It deletes the local data.csv file after tracking.

D. It commits the data file directly to Git.

Solution

1. Step 1: Understand dvc add function

   The dvc add command tracks the data file and creates a small pointer file (like data.csv.dvc) to represent it.

2. Step 2: Clarify what dvc add does not do

   It does not upload data to remote storage (that's dvc push), nor delete the local file or commit to Git directly.

3. Final Answer:

   It tracks the data file data.csv in DVC and creates a pointer file. -> Option A

4. Quick Check:

   dvc add tracks data locally = A [OK]

Hint: dvc add tracks data locally, dvc push uploads [OK]

Common Mistakes:

• Confusing dvc add with dvc push
• Thinking it deletes local data
• Assuming it commits data to Git

4.

Given this error when trying to push data versions:

Error: failed to push data to remote storage: permission denied

What is the most likely cause and fix?

medium

A. Git repository is not initialized; fix by running git init.

B. The local data file is missing; fix by adding the file again.

C. DVC is not installed; fix by reinstalling DVC.

D. The remote storage credentials are missing or incorrect; fix by configuring access keys.

Solution

1. Step 1: Analyze the permission denied error

   This error usually means the remote storage (like S3, GCS) credentials are missing or wrong.

2. Step 2: Identify the correct fix

   Configuring or updating access keys or permissions for the remote storage resolves this issue.

3. Final Answer:

   The remote storage credentials are missing or incorrect; fix by configuring access keys. -> Option D

4. Quick Check:

   Permission denied = fix credentials [OK]

Hint: Permission denied usually means remote access keys need fixing [OK]

Common Mistakes:

• Assuming local file is missing
• Thinking Git init fixes remote errors
• Believing DVC installation causes permission errors

5.

In a team working on machine learning, why is good data versioning critical compared to just versioning code?

Choose the best explanation.

hard

A. Because data changes impact model training results, and tracking data versions ensures reproducibility and reliable improvements.

B. Because code versioning tools cannot handle any files larger than 1MB.

C. Because data versioning replaces the need for code versioning entirely.

D. Because data versioning automatically fixes bugs in the code.

Solution

1. Step 1: Understand the role of data in ML models

   Data directly affects how models learn and perform, so knowing exactly which data version was used is essential.

2. Step 2: Explain why data versioning matters for teams

   Good data versioning helps teams reproduce results and improve models reliably by tracking data changes alongside code.

3. Final Answer:

   Because data changes impact model training results, and tracking data versions ensures reproducibility and reliable improvements. -> Option A

4. Quick Check:

   Data affects models; versioning ensures reproducibility = B [OK]

Hint: Data versioning ensures model results can be repeated and improved [OK]

Common Mistakes:

• Thinking data versioning replaces code versioning
• Believing code tools can't handle files over 1MB
• Assuming data versioning fixes code bugs