What Is Version Control?

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Version control tracks and manages changes in programs, websites, and files in general. In software development, the process involves recording every modification done to the source code, typically with timestamps.  

Developers use version control systems to keep track of any changes made. The systems allow the teams to return to previous versions and compare them to newer ones, improving product uptime with quick reversion and resulting in more stable operations. 

 

How Version Control Systems Work

The type of version control system chosen determines how it operates. There are three main types:

1. Local: In localized version control systems, the programmer copies different versions of the codebase to a local computer’s database. Referring to these versions lets them see what the file previously looked like. However, you would have to copy the database to another computer for another team member to use. 

2. Centralized: Multiple computers can access a single central database in centralized version control systems. All versions created will be stored in it. While this saves the team from copying to several computers, it would also mean they cannot work if the central database is down. 

3. Distributed: The complete codebase and its version history are mirrored on connected computers for distributed version control systems. This allows developers the ability to work offline. It also adds additional security because there are multiple copies, and you aren’t relying on a single backup. 

 

Benefits of a Version Control System

Implementing version control systems offer several advantages such as:

• Improved project development speed. It allows the dev team to work faster as they can quickly access older versions of the code and revise the current one as needed. 

• It serves as a backup in disaster recovery measures. If anything happens to the current version, the team can still work with a previous version to restore it. 

• Reduced margin for errors. In addition, access to earlier versions allows developers to trace all the changes to minimize conflicts in the current file. 

Version Control for Machine Learning Pipelines

In machine learning, there are two types of information being changed and updated simultaneously: The code that makes up a machine learning model, and the datasets that are being processed by the machine learning model. Version control systems exist for models and for data, and they take a variety of approaches to how version history is stored and managed in the system. 

These include tools like metadata stores, model registry, and feature store. 

 

 

Version Control in Pachyderm

Pachyderm handles version control by breaking down the jobs for your machine learning model at the datum level. The datum refers to the smallest unit of data and code needed to run a complete processing job within your dataset. If you are running computer vision, for example, this would be an individual image file and all of the code needed to process that file through your model. 

This iterative approach to data version control means that Pachyderm can look at all of your data, and see the datum-level changes in your files, only processing what is new or changed – saving you processing time and reducing your cloud computing bills by only processing what’s needed.

Pachyderm’s data version control system allows for automated file tracking and complete audits, allowing you to trace back all changes to your data. See our version control in action when you book a demo and tech consultation call for your team. 

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