Why data versioning is harder than code versioning in MLOps - Why It Works

Introduction

Tracking changes in data is more difficult than tracking changes in code because data files are often large, binary, and frequently updated. Unlike code, data can be messy, have many versions, and require special tools to manage efficiently.

When you want to keep track of different versions of datasets used in machine learning experiments

When you need to reproduce a model training exactly with the same data snapshot

When multiple team members update or add data and you want to avoid conflicts or data loss

When you want to audit or compare changes in data over time to understand model performance shifts

When you want to store large datasets efficiently without duplicating entire files for every change

Commands

Initialize a Git repository to track code changes. This works well for small text files like code but not for large data files.

Terminal

git init

Expected Output

Initialized empty Git repository in /home/user/project/.git/

Add a data file to Git tracking. This is possible but inefficient for large or frequently changing data files.

Terminal

git add data.csv

Expected Output

No output (command runs silently)

Commit the data file to the Git repository. Git stores the entire file each time it changes, which can quickly use a lot of space.

Terminal

git commit -m "Add initial data file"

Expected Output

[master (root-commit) abc1234] Add initial data file

 1 file changed, 0 insertions(+), 0 deletions(-)

 create mode 100644 data.csv

Initialize DVC (Data Version Control) in the project. DVC is designed to handle large data files efficiently by storing metadata and linking to external storage.

Terminal

dvc init

Expected Output

Initialized DVC repository.

You can now track data files with 'dvc add <filename>'.

Tell DVC to track the data file. DVC creates a small metafile to track the data version without storing the whole file in Git.

Terminal

dvc add data.csv

Expected Output

Adding 'data.csv' to DVC tracking.

Computing checksum...

Saving information to 'data.csv.dvc'.

Add the DVC metafile and updated .gitignore to Git. This keeps Git tracking small and efficient.

Terminal

git add data.csv.dvc .gitignore

Expected Output

No output (command runs silently)

Commit the DVC metafile to Git. The actual data is stored separately, making versioning large files practical.

Terminal

git commit -m "Track data.csv with DVC"

Expected Output

[master abc5678] Track data.csv with DVC

 2 files changed, 10 insertions(+), 2 deletions(-)

 create mode 100644 data.csv.dvc

Key Concept

If you remember nothing else from this pattern, remember: data files are large and change differently than code, so special tools like DVC are needed to version them efficiently.

Common Mistakes

Trying to version large data files directly with Git

Git stores full copies of files on each change, causing slow performance and huge repositories.

Use data versioning tools like DVC that track data changes efficiently without storing full copies in Git.

Ignoring .gitignore updates when adding data files

Git may try to track large data files directly, causing repository bloat and slow operations.

Update .gitignore to exclude large data files and track only metadata files with Git.

Summary

Git works well for code but is inefficient for large or frequently changing data files.

Data versioning tools like DVC track data changes by storing metadata and linking to external storage.

Combining Git for code and DVC for data keeps repositories small and reproducible.

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