Why DVC is Essential for Data Science and Machine Learning Teams

Recently, as data is being generated at an unprecedented rate and AI is advancing at an exponential rate, businesses across various industries are leveraging all this to gain insights and make informed decisions. Data science and machine learning are rapidly becoming essential tools in this process. However, working with large datasets and complex models can be challenging, particularly when it comes to version control, collaboration, and reproducibility.

This is where Data Version Control (DVC) comes in. DVC is an open-source tool that enables data scientists and machine learning teams to manage their data and models efficiently. I first stumbled upon DVC during my internship with Zero One Group at summer after my second year of undergraduate studies and after experiencing its capabilities firsthand, I realised just how significantly it can revolutionise most data science and ML projects.

What is DVC?

DVC is a version control system for data science and machine learning projects. It allows users to track changes to datasets, models, and experiment configurations, similar to Git, a popular version control system used in software development. However, DVC focuses specifically on managing large datasets and machine learning models, which are often too large to be stored in Git.

DVC also provides users with a command-line interface, making it easy to integrate into existing workflows. It also integrates with Git, allowing users to version control their data alongside their code.

It also integrates CI/CD for machine learning (more here), which is pretty cool :)

How Does DVC Work?

DVC works by creating a metafile, which contains the data's metadata and a pointer to where the data is stored. This metafile is version controlled, allowing users to track changes to the dataset over time.

When a user wants to work with a particular version of the dataset, they can use DVC to download the required files. DVC also supports cloud-based storage solutions like AWS S3 or Google Cloud Storage, which allows users to store their datasets remotely and access them easily.

DVC works similarly for machine learning models. Users can version control their models and their training configurations using DVC. This makes it easy to reproduce experiments, share models with colleagues, and collaborate on projects.

Quick DVC Setup Tutorial

A quick step-by-step tutorial on how to use DVC:

1. Install DVC by using pip and running pip install dvc
2. Initialize DVC in your project directory by running dvc init. This will create a .dvc directory in your project directory, which is where DVC stores its metafiles.
3. Add data to DVC by running dvc add <path_to_data_file>. This will create a .dvc file for the data file you added, which contains metadata and a pointer to where the data is stored.
4. Version control your data with DVC by committing the .dvc files to Git using the commands git add .dvc followed by git commit -m "Added data to DVC". This will commit the .dvc files to Git, allowing you to track changes to your data over time.
5. Download data from DVC by running dvc pull. This will download the data files from their remote storage location and place them in your project directory. 6.Add a machine learning model to DVC by running dvc add <path_to_model_file>. This will create a .dvc file for the model file you added, which contains metadata and a pointer to where the model is stored.
6. Version control your models with DVC by committing the .dvc files to Git running git add .dvc followed by git commit -m "Added model to DVC". This will commit the .dvc files to Git, allowing you to track changes to your models over time.
7. Finally, to reproduce experiments using DVC, you can run dvc repro. This will reproduce the experiment using the same data and model versions that were used when the experiment was originally run. This helps ensure reproducibility and makes it easy to share experiments with colleagues.

There is a more detailed and documented tutorial here if you are interested!

Key benefits of DVC

The following are some of the significant advantages of integrating DVC into your data science and ML projects:

Reproducibility

DVC ensures that all experiments are reproducible. This means that other team members can reproduce the same results using the same data and models, even if they were generated at different times. Reproducibility is essential in data science, as it helps to ensure the accuracy and consistency of results.

Version Control

DVC provides version control for datasets and machine learning models. This allows users to track changes over time, revert to earlier versions, and collaborate on projects without conflicts. Version control is essential in data science, as it helps to ensure that all changes are tracked and documented.

It also integrates with Git, which is a huge plus ;)

Collaboration

DVC makes it easy for teams to collaborate on data science and machine learning projects. Teams can share datasets and models without worrying about file size limitations. DVC also makes it easy to share project configurations, experiment results, and other project-related information.

Efficiency

DVC allows users to store datasets remotely, which saves storage space on local machines. This can be particularly beneficial when working with large datasets that would otherwise take up too much disk space. DVC also supports parallel execution, which can help to speed up experiments.

Scalability

DVC scales easily, making it suitable for small and large projects alike. It can be used on a single machine or distributed across a cluster of machines. DVC also integrates with cloud-based storage solutions, which makes it easy to scale up as data volumes grow.

Conclusion

DVC is a powerful tool that provides version control, collaboration, and reproducibility for datasets and models, making it easy for teams to work together and ensure that all changes are tracked and documented.

I hoped I managed to convince you why DVC is essential in today's data science and machine learning projects. Thank you for reading and I hope you don't forget about this underrated Git version of Big Data :)