Best Practices for Version Controlling ML Projects

This article discusses the importance of version control in machine learning projects and provides guidance on how to choose and set up a version control system. It also covers DVC, a version control system designed specifically for managing large datasets in ML projects. The article concludes with best practices for version-controlling ML models, as well as collaboration and code review tips.


Version control is an essential tool for managing software projects, including those in the field of machine learning (ML). Version control systems enable data scientists and developers to collaborate on code, track changes, and maintain a history of revisions. In this article, we will explore best practices for version-controlling ML projects, including choosing a version-control system, setting up a repository, managing code and data, collaborating, and more.

What is Version Control?

Version control is the process of managing changes to a set of files over time. It allows developers to keep track of changes, collaborate with others, and revert to previous versions if necessary. Version control systems (VCS) such as Git and SVN are commonly used in software development to track changes to code. In ML projects, version control is also used to track changes to datasets, preprocessing scripts, training scripts, and model files.

Why Version Control is Important in ML Projects?

Version control is particularly important in ML projects due to the large volumes of data involved and the iterative nature of model development. Without version control, it can be challenging to keep track of changes to datasets, preprocessing steps, and model architectures. Version control also enables reproducibility, which is essential for research and development. Reproducibility allows other researchers to validate results, compare methods, and build upon existing work. Version control also facilitates collaboration by enabling multiple team members to work on the same project simultaneously and manage changes effectively.

Choosing a Version Control System

There are several version control systems available, including Git, SVN, and Mercurial. Git is by far the most popular version control system, and it has become the de facto standard for version controlling ML projects. Git is distributed, which means that each user has a complete copy of the repository, making it easy to collaborate and work offline. Git also offers a range of powerful features, such as branching and merging, which make it well-suited to managing complex ML workflows.

When choosing a version control system for an ML project, it is important to consider factors such as ease of use, scalability, and compatibility with existing tools and workflows. Git is a good choice for most ML projects, but it may not be the best option for all projects. It is worth considering other options such as SVN or Mercurial if there are specific requirements that Git cannot meet.

Setting up a Version Control System

Setting up a version control system for an ML project typically involves creating a repository and defining a workflow. The following steps provide a basic guide to setting up a Git repository:

Step 1: Create a new repository on GitHub or a similar service. Step 2: Clone the repository to your local machine using the **git clone** command. Step 3: Create a new branch using the **git checkout -b** command. Step 4: Add your code, data, and other files to the branch using the **git add** command. Step 5: Commit your changes using the **git commit** command. Step 6: Push your changes to the remote repository using the **git push** command.

It is also important to define a workflow for managing changes to the repository. This could involve using branches for different features or experiments, and merging changes back into the main branch once they have been tested and validated.

How to do Version Control with DVC?

DVC is a version control system designed specifically for ML projects. It allows users to version control large datasets and machine learning models while avoiding the storage and computational overheads of traditional VCS. Here are the steps to use DVC for version control in your ML project:

Step 1: Install DVC DVC can be installed using pip or conda, depending on your environment. Here's how to install DVC using pip:

pip install dvc

Step 2: Initialize DVC in your project directory To start using DVC, navigate to your project directory and initialize it with the following command:

dvc init

This command creates a .dvc directory in your project directory, which contains configuration files and metadata used by DVC.

Step 3: Create a DVC remote A DVC remote is a storage location where DVC stores your data and model files. You can create a DVC remote on your local machine or a cloud storage provider such as AWS S3, Google Cloud Storage, or Microsoft Azure. Here's an example of how to create a local DVC remote:

bashCopy code
dvc remote add myremote /path/to/myremote

Step 4: Add data and model files to DVC To version control a file in DVC, you first need to add it to DVC using the dvc add command. For example, to add a dataset file to DVC, use the following command:

dvc add data/train.csv

This command creates a .dvc file in your project directory, which contains metadata about the added file, including its hash and the DVC remote where it's stored.

Step 5: Commit changes to DVC Once you've added files to DVC, you can commit changes using the **dvc commit** command. This command creates a Git commit and updates the DVC metadata with the new changes. For example, to commit changes to the dataset file we added earlier, use the following command:

dvc commit data/train.csv.dvc -m "Add train dataset"

Step 6: Push changes to DVC remote To push your changes to a DVC remote, use the dvc push command. This command uploads your data and model files to the remote storage and updates the DVC metadata with the new location. For example, to push the train dataset file to the remote storage, use the following command:

dvc push data/train.csv.dvc

Step 7: Pull changes from DVC remote To pull changes from a DVC remote, use the dvc pull command. This command downloads the data and model files from the remote storage and updates the DVC metadata with the new location. For example, to pull the train dataset file from the remote storage, use the following command:

bashCopy code
dvc pull data/train.csv.dvc

Best Practices for Version-Controlling ML Models

Version controlling ML models involves version controlling code, data, and experiment results. Here are some best practices to consider:

  • Version control code using Git or a similar version control system.
  • Version control data using a tool like DVC (see section 5 for more information on DVC).
  • Track experiment results, such as performance metrics and visualizations, using a tool like MLflow or TensorBoard.
  • Use descriptive commit messages to provide context for changes to the code or data.
  • Use tags or releases to mark significant milestones in the development of the model.

What is DVC and 5 Good Reasons to Use It?

DVC (Data Version Control) is a version control system designed specifically for managing large datasets in ML projects. DVC works alongside Git and other version control systems to manage data as well as code.

Here are 5 good reasons to use DVC:

  1. Efficient version control of large datasets: DVC uses a Git-like interface to version control large datasets, which can be difficult to manage using traditional version control systems.
  2. Reproducibility: DVC enables users to reproduce experiments by tracking not only code changes but also changes to the data used in those experiments.
  3. Data sharing and collaboration: DVC makes it easy to share and collaborate on datasets by tracking changes and enabling users to pull and push data changes.
  4. Integration with ML workflows: DVC integrates with popular ML frameworks such as TensorFlow, PyTorch, and scikit-learn, making it easy to version control entire ML workflows.
  5. Data pipeline management: DVC can be used to manage complex data pipelines, ensuring that data is processed and transformed consistently across experiments.

Collaboration and Code Review

Collaboration is a key aspect of version control, and there are several best practices to consider when collaborating on an ML project:

  • Use pull requests for code review: Pull requests enable team members to review changes to code and data before they are merged into the main branch.
  • Use a code style guide to maintain consistency across the project.
  • Use tools such as GitHub Issues or Jira to track tasks and issues.
  • Establish guidelines for documentation, such as README files or project wikis.
  • Use continuous integration and deployment (CI/CD) tools to automate testing and deployment.


Version control is a critical tool for managing changes to code and data in ML projects. DVC is a powerful open-source tool designed for version controlling large datasets and machine learning models. By using DVC, you can version control your ML project effectively, enabling reproducibility, collaboration, and data lineage tracking.

Key Takeaways

  1. Version control is important for managing changes to code and data in ML projects.
  2. Choosing the right version control system is crucial for effective version control.
  3. Setting up a version control system involves creating a repository, tracking changes, and managing code and data.
  4. DVC is a version control system designed specifically for managing large datasets in ML projects.
  5. DVC enables efficient version control, reproducibility, data sharing and collaboration, integration with ML workflows, and data pipeline management.
  6. Best practices for version-controlling ML models include using descriptive commit messages, using branches and tags, tracking changes to data, and version-controlling experiments.
  7. Collaboration and code review are crucial for effective version control in ML projects.
  8. Using pull requests, code style guides, issue tracking tools, documentation, and CI/CD tools can improve collaboration and code review.
  9. Following best practices for version control can ensure that ML projects are well-managed, reproducible, and easily scalable.


1. What is version control?

A) A way to keep track of code changes over time. 

B) A way to manage large datasets in machine learning projects. 

C) A way to create multiple versions of a project. 

D) A way to optimize machine learning models.

Answer: A

2. Which of the following is a benefit of using DVC for version control in machine learning projects?

A) Improved model performance 

B) Faster data processing 

C) Better data sharing and collaboration 

D) Easier hyperparameter tuning

Answer: C

3. What is a best practice for version-controlling machine learning models?

A) Using non-descriptive commit messages 

B) Not tracking changes to data 

C) Version-controlling experiments 

D) Not using branches and tags

Answer: C

4. Why is collaboration important for effective version control in machine learning projects?

A) To improve model accuracy 

B) To track changes to code and data 

C) To ensure reproducibility 

D) To speed up data processing

Answer: C

Module 2: Version Control for MLBest Practices for Version Controlling ML Projects

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