Why data versioning is harder than code versioning in MLOps - The Real Reasons

The Big Idea

Discover why managing data versions feels so tricky compared to code--and how to fix it!

The Scenario

Imagine you are working on a project where you need to keep track of changes in your code and data separately. You use a simple folder to save your data files and a Git repository for your code. Every time you update your data, you manually copy new files into the folder and try to remember which version you used for each experiment.

The Problem

This manual way is slow and confusing. Data files are often large, so copying them wastes time and space. It's easy to lose track of which data version matches which code version. Mistakes happen, like using the wrong data for training, leading to wrong results and frustration.

The Solution

Data versioning tools automatically track changes in data files, just like Git does for code. They store only differences to save space and link data versions to code versions. This makes it easy to reproduce experiments and share exact data states without confusion or extra copying.

Before vs After

✗ Before

Copy data files manually to new folders for each version

✓ After

Use a data versioning tool to track and switch data versions automatically

What It Enables

It enables reliable and efficient tracking of data changes, making experiments reproducible and collaboration smooth.

Real Life Example

In a machine learning project, you can switch between different cleaned datasets easily to compare model results without mixing up files or wasting storage.

Key Takeaways

Manual data tracking is slow, error-prone, and wastes space.

Data versioning tools automate tracking and save storage by storing differences.

This leads to reproducible experiments and easier collaboration.