Top Data Version Control Tools for Machine Learning Research in 2022

All systems used for production must be versioned. A single location where users can access the most recent data. An audit trail must be created for any resource that is often modified, especially when numerous users are making changes at once.

To ensure everyone on the team is on the same page, the version control system is in charge. It ensures that everyone on the team is collaborating on the same project at once and that everyone is working on the most recent version of the file. You can complete this task quickly if you have the right tools!

If you employ a dependable data version management method, you’ll have consistent data sets and a complete archive of all your research. Data versioning solutions are essential for your workflow if you are concerned about repeatability, traceability, and the history of ML models.

They assist you in obtaining a copy of an object, like a hash of a dataset or model, which you may use to distinguish and contrast. This data version is frequently recorded into your metadata management solution to ensure that your model training is versioned and repeatable.

It’s time to examine the best data version control tools on the market so you can keep track of each component of your code.

Git LFS

Use of the Git LFS project is unrestricted. Git saves the contents of large files on a remote server, such as GitHub.com or GitHub Enterprise, and substitutes large files with text pointers. Large files, including audio samples, films, databases, and photographs, are among the types of files that are replaced.

It enables you to use Git to swiftly clone and retrieve large file repositories, host more files in your Git repositories using external storage, and version huge files up to several GB in size. This is a relatively simple solution in terms of data handling. You don’t need other toolkits, storage systems, or scripts to work with Git. It restricts the amount of data you download. This suggests that copying huge files will be quicker than obtaining them from repositories. The points point to the LFS and are made of a lighter material.

LakeFS

With a Git-like branching and committing methodology that scales to petabytes, LakeFS is an open-source data versioning solution that stores data in S3 or GCS. This branching method makes your data lake ACID compliant by enabling modifications to occur in separate branches that can be created, merged, and rolled back atomically and instantly.

Teams may develop repeatable, atomic, and versioned data lake activities with LakeFS. Although it is new to the scene, it is a force to be taken seriously. It interacts with your data lake using a Git-like branching and version management method and is scaleable up to Petabytes of data. You may check for version control on an exabyte scale.

DVC

Data Version Control is an accessible data versioning solution for data science and machine learning applications. You can define your pipeline with this application in any language.

DVC is not solely focused on data versioning, as its name suggests. The tool makes machine learning models shared and reproducible by managing big files, data sets, machine learning models, code, etc. Additionally, it makes it easier for teams to manage pipelines and machine learning models. The application follows Git’s example by offering a straightforward command line that can be configured quickly.

Finally, DVC will help to increase the repeatability and consistency of your team’s models. Use Git branches to test new ideas rather than the code’s convoluted file suffixes and comments. Use automatic metric tracking instead of paper and pencil when traveling.

You can use push/pull commands rather than ad-hoc scripts to transfer consistent bundles of machine learning models, data, and code into the production environment, remote machines, or a colleague’s desktop.

DeltaLake

An open-source storage layer called DeltaLake increases data lake dependability. In addition to supporting batch and streaming data processing, Delta Lake also offers scalable metadata management. It rests on your current data lake and uses the Apache Spark APIs. Thanks to Delta Sharing, the first open protocol for secure data sharing in business, it is simple to exchange data with other companies independent of their computer systems.

Delta Lakes’s architecture is one that can read batch and stream data. Petabytes of data can be handled with ease by Delta Lakes. Users can access metadata using the Describe Detail method, which is stored in the same manner as data.

Using Delta makes upserts straightforward. Similar to SQL Merges, these upserts or merges into the Delta table. It allows you to edit, insert, and delete data and integrate data from another data frame into your table.

Dolt

Dolt is a SQL database that functions similarly to a git repository, forking, cloning, branching, merging, pushing, and pulling. Dolt enables data and structure to change simultaneously to enhance the user experience of a version control database.

It’s a fantastic tool for teamwork between you and your coworkers. You can use SQL commands to conduct queries or alter the data in Dolt like you would with any other MySQL database.

Dolt is unique when it comes to data versioning. Unlike some other systems that only version data, Dolt is a database. Although the application is currently in its early stages, full integration with Git and MySQL is soon to be achieved.

With Dolt, you can use any command that you are accustomed to using with Git. File versions using Git, tables using Dolt Import CSV files, commit your changes, publish them to a remote, and combine your teammate’s changes using the command line interface.

Pachyderm

Pachyderm is a robust, free version control system for data science. Pachyderm Enterprise is a powerful data science platform for extensive teamwork in highly secure settings.

One of the few data science platforms on the list is Pachyderm. The mission of Pachyderm is to offer a platform that controls the entire data cycle and makes it simple to reproduce the results of machine learning models. In this sense, Pachyderm is referred to as “the Docker of Data.” Your execution environment is packaged by Pachyderm using Docker containers. This makes it straightforward to obtain the same outcomes again.

Versioned data and Docker enable data scientists and DevOps teams to deploy models confidently. A practical storage system may maintain petabytes of organized and unstructured data while minimal storage expenses.

File-based versioning offers a complete audit trail for all data and artifacts, including intermediate outputs, throughout the pipeline phases. These pillars are the foundation for many of the tool’s capabilities, enabling teams to make the most of it.

Neptune

The ML metadata store, a crucial component of the MLOps stack, manages model-building metadata. Neptune serves as a consolidated metadata store for each MLOps workflow.

Thousands of machine learning models can all be tracked, shown, and compared in one location. It has a collaborative interface and capabilities, including experiment tracking, model registry, and model monitoring. It integrates more than 25 tools and libraries, including several tools for hyperparameter tuning and model training. Neptune registration is possible without using a credit card. Its place will be filled by a Gmail account.

Mercurial

A distributed source control management solution with an easy-to-use interface, Mercurial (Hg) is free and open-source. Hg is a platform-independent tool created in Python. A quick, simple-to-use gadget that doesn’t need upkeep. It is simple for non-technical contributors with good documentation. It has enhanced security capabilities. However, since previous commits cannot be edited, it lacks change control.

CVS

You can handle several source code versions using CVS (Concurrent Version System). Sharing version files through a shared repository on the platform makes it simple for your team to work together. CVS doesn’t make numerous copies of your source code files like other programs. Instead, it preserves just one copy of the code while keeping track of any alterations. High reliability because it forbids commits that contain errors. Code reviews are simplified because it just records changes made to the code.

Lightrun

Open-source web interface and observability platform Lightrun uses Git-like practices. Every move and modification made by your team is recorded and easily auditable. To fix errors faster in any scenario, you can add logs, analytics, and traces to your app in real-time and on demand. It offers essential security features like blocklisting, a strengthened authentication mechanism, and an encrypted communication channel. It includes strong observability abilities. Works well with apps, resulting in zero downtime. Debugging time can be considerably decreased. Simple procedures based on commands

HelixCore

The version control program from Perforce is called HelixCore. Through the tracking and management of changes to source code and other data, it streamlines the development of complicated products. Your configuration changes are branched and merged using the Streams feature. HelixCore is highly scalable and makes it simple to look into change history. It has a native command-line tool included. The capacity to integrate with outside agencies. Multiple authentications and access features for better security

Liquibase

Liquibase is a database version control solution that relies on migrations and uses changelog capability to keep track of database modifications. Its XML-based changeset definitions let you operate the database schema on various platforms. There are two versions available: open-source and premium. Permits specific rollbacks to reverse modifications. Supports several different types of databases. Allows for the specification of updates in a variety of forms, including SQL, XML, and YAML

Note: We tried our best to feature the best Data Version Control Tools available, but if we missed anything, then please feel free to reach out at Asif@marktechpost.com 
Please Don't Forget To Join Our ML Subreddit

Prathamesh Ingle is a Consulting Content Writer at MarktechPost. He is a Mechanical Engineer and working as a Data Analyst. He is also an AI practitioner and certified Data Scientist with interest in applications of AI. He is enthusiastic about exploring new technologies and advancements with their real life applications