Data versioning (DVC) in ML Python - ML Experiment: Train & Evaluate

Experiment - Data versioning (DVC)

Problem:You have a machine learning project where the dataset changes often. You want to keep track of different versions of your data so you can reproduce your results exactly.

Current Metrics:No data versioning is used. Dataset changes cause inconsistent model training results and difficulty in reproducing experiments.

Issue:Without data versioning, it is hard to know which data version was used for a specific model run. This causes confusion and unreliable results.

Your Task

Implement data versioning using DVC to track dataset changes and ensure reproducible model training.

Use DVC commands only for data versioning.

Do not change the model architecture or training code.

Keep the dataset size manageable for quick testing.

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Solution

ML Python

import os

# Step 1: Initialize DVC in the project folder
os.system('dvc init')

# Step 2: Add dataset folder to DVC tracking
os.system('dvc add data/')

# Step 3: Commit changes to Git
os.system('git add data.dvc .gitignore')
os.system('git commit -m "Add dataset to DVC tracking"')

# Step 4: (Optional) Push data to remote storage if configured
# os.system('dvc remote add -d myremote s3://mybucket/path')
# os.system('dvc push')

# Now you can train your model using the tracked data version
# To switch data versions, use 'dvc checkout <version>' in terminal

print("DVC data versioning setup complete.")

Initialized DVC in the project folder to enable data version control.

Added the dataset folder to DVC tracking to record data versions.

Committed DVC metadata files to Git for version tracking.

Provided commands to push data to remote storage for backup and sharing.

Results Interpretation

Before: No data versioning, dataset changes cause inconsistent results and confusion.

After: Dataset versions tracked with DVC, enabling reproducible experiments and clear data history.

Data versioning with DVC helps keep track of dataset changes, making machine learning experiments reproducible and reliable.

Bonus Experiment

Try configuring a remote DVC storage (like AWS S3 or Google Drive) and push your data versions there.

💡 Hint

Use 'dvc remote add -d <name> <url>' to add remote storage, then 'dvc push' to upload data versions.