An Explanation of Version Control?Software

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Working on a code for a program is a lengthy process that takes a lot of commitment in order to complete. The longer it takes to program, the more developers become more prone to mistakes. To mitigate this version control software is needed.

Version Control?Software

Atlassian Bitbucket defines version control as the practice of tracking and managing changes to software code. It further expands saying that the source code is extremely valuable so versioning is needed to prevent catastrophes. It is significant because it makes it easier for users and software providers to keep track of the previous versions of code. Since consumers want a logical manner to understand when and what updates are made, and rely on software developers to keep them up to date, version control is essential for software businesses to be able to keep track of what is now different.

Advantages of version?control

Lance Harvie states that there are several advantages for the use of version control tools such as:

1. Easy Modification of the codebase: programs that continuously change are a part of software development, and version control systems facilitate this process by storing source code, documentation, and configuration files.
2. Reverting Errors: Errors that happen in new code can be restored to previous versions of the code
3. Collaboration: It can help increase collaboration between programmers as they have access to the old code that they can build upon.
4. Backup: A version control repository acts as a backup in case the servers provisioning the system goes down

Model Versioning Tools

With all the advantages that come with versioning the tools for versioning are important to know. Here are 6 model versioning tools that businesses and developers can adopt according to Akinwande Komolafe:

1. MLFlow: MLFlow is our preferred model versioning tool at Eden AI. It allows Machine Learning Engineers to manage the ML lifecycle. It lets you version data and models, repackage code for reproducible runs and integrates well with ML libraries and tools like TensorFlow, Pytorch, XGBoost as well as Apache Spark. It is also well adopted within the AI industry.
2. Neptune: Primarily built for ML and data science teams to run and track experiments. It allows you to store, version, query, and organise models and associated metadata.
3. ModelDB: An open-source MLOps tool that allows you to version your implementation code, data, and model artefacts and It lets you manage models and pipelines built in different programming languages in their native environments and configurations.
4. DVC (Data Version Control): This is an open-source MLOps tool that allows for version control of training sets, model artefacts, and metadata quickly and efficiently.
5. Pachyderm: Similar to DVC however supports four checkpoints in the ML workflow: data preparation, Experimentation, training, and deployment into production.
6. Polyaxon: This platform provides ML packages and algorithms for scalable and reproducible functionalities. Runs all ML and DL libraries allowing you to push ideas efficiently into production.

Version control software is used to manage the source code of ML projects and is one of the many tools that developers have. It tracks changes to the codebase, allows for collaboration, and enables developers to revert to previous versions of the code. To know which other tools can help you contact us @[email protected].