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GitHub has long been the go-to platform for developers, including those in the data science community. It offers robust version control and collaboration features. However, data scientists often have unique requirements, such as handling large datasets, complex workflows, and specific collaboration needs that GitHub may not fully cater to. This has led to the rise of alternative platforms, each offering distinctive features and advantages.
In this blog, we explore the top five GitHub alternatives that are particularly suited for data science projects, providing diverse options for collaboration, project management, and data and model handling.
Kaggle is renowned in the data science community for its unique combination of data science competitions, datasets, and a collaborative environment.
The platform offers access to a vast repository of datasets and an opportunity for data scientists to test their skills in real-world scenarios through competitions. Moreover, I provide access to edit, run, and share code notebooks with outputs.
Image from Kaggle
I have been using Kaggle for three years now, and I absolutely love it. This platform allows me to quickly run deep learning projects on free GPUs and TPUs. With its help, I have been able to create a strong portfolio by sharing my analytical reports and machine learning projects. Additionally, I have participated in various data analytics and machine learning competitions, which has helped me improve my skills in these areas. Overall, Kaggle has been an excellent resource that has enabled me to grow both personally and professionally.
If you are a beginner in data science, I highly recommend starting with Kaggle instead of GitHub. Kaggle offers a wide range of free features that are essential for any data science project. Additionally, you can learn from others and ask questions directly in a community of like-minded individuals who want to help each other.
Image from Kaggle 2. Hugging Face
Hugging Face has rapidly become a center for the newest developments in natural language processing (NLP) and machine learning. It sets itself apart by offering a vast collection of pre-trained models, along with a collaborative ecosystem for training and sharing new models. Additionally, it has become effortless to upload your dataset and deploy your machine learning web app for free.
In Hugging Face, a model repository is similar to GitHub and contains various types of information, including files and models. You can attach a research paper, add performance metrics, build a demo with the model, or create an inference. Additionally, you can now comment and submit pull requests, just like in GitHub.
Image from Hugging Face
I use Hugging Face frequently to deploy models, upload trained models, and build a strong machine learning portfolio. I have implemented deep reinforcement learning, multilingual speech recognition, and large language models.
This platform is primarily designed for the community, and one of its most important features is that it offers most of its features for free. However, if you have a state-of-the-art model, you can even request paid features. This makes it the go-to platform for anyone who aspires to become an ML engineer or NLP engineer.
Image from Hugging Face 3. DagsHub
DagsHub is a platform tailor-made for data scientists and machine learning engineers, focusing on the unique needs of managing and collaborating on data science projects. It offers exceptional tools for versioning not just code but also datasets and ML models, addressing a common challenge in the field.
The platform integrates well with popular data science tools, allowing for a smooth transition from other environments. DagsHub's standout feature is its community aspect, offering a space for data scientists to collaborate and share insights, making it a particularly attractive choice for those looking to engage with a community of peers.
Image from DagsHub
I am a huge fan of DagsHub due to its user-friendly approach in uploading and accessing data and models. DagsHub provides both a simple API and a GUI that allows you to upload and access data and models with ease. Moreover, it offers MLFlow instances for experiment tracking and model registry. Additionally, it provides a free instance of Label Studio to label your data. It's an all-in-one platform for all your machine learning requirements. DagsHub also offers third-party integrations such as S3 bucket, New Relic, Jenkins, and Azure blob storage.
Image from DagsHub 4. GitLab
GitLab is a good alternative to GitHub for all kinds of tech professionals. It offers robust version control and collaboration, CI/CD, Project Management and Issue Tracking, Security and Compliance, Analytics and Insights, Webhooks and REST API, Pages, and more.
This platform is an ideal solution for developers and data scientists who need to build seamless workflow automation, from data collection to model deployment. It also offers powerful issue tracking and project management tools, which are essential for coordinating complex data science projects.
Image from GitLab
I have been using GitLab for the past three years, primarily to familiarize myself with the platform and to migrate my static websites from GitHub to GitLab. GitLab's user interface is easy to understand and it offers a wide range of tools for free users. Moreover, you have the option to host your own GitLab Community Edition instance for free, giving you complete control over your projects.
Just like GitHub, GitLab can also be used as a portfolio for your data science projects. You can upload and share all of your work in one place, and it even has better collaboration tools for larger and more complex projects. GitLab is a powerful platform that you should definitely consider, even if you're already satisfied with GitHub.
Image from GitLab 5. Codeberg
Codeberg.org sets itself apart as a non-profit, community-driven platform that puts a strong emphasis on open source and privacy. It offers a simple, user-friendly interface that appeals to those looking for an uncomplicated and straightforward code hosting solution. For data scientists who prioritize open-source values and data privacy, Codeberg presents an attractive alternative.
Image from Codeberg
It offers CI/CD solutions, Pages, SSH and GPG, webhooks, third-party integrations, and collaboration tools for projects of all types, similar to GitHub.
While installing Librewolf, I discovered Codeberg and Forgejo. They provide a GitHub-like experience with Git and simplified workflow automation. I highly recommend giving them a try for hosting your projects.
Image from Codeberg Conclusion
Each of these platforms offers unique features and advantages for data scientists. GitLab excels in integrated workflow management, DagsHub and Hugging Face is tailored for machine learning project hosting and collaboration, Kaggle provides an interactive environment for learning and competition, and Codeberg emphasizes open source and privacy. Depending on their specific needs, whether it's advanced project management, community engagement, specialized tools, or a commitment to open-source principles, data scientists can find a suitable alternative to GitHub among these options.
Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in Technology Management and a bachelor's degree in Telecommunication Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.
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