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GitHub has long been the go-to platform for developers, including those in the data science community. Offers strong collaboration and version control features. However, data scientists often have unique requirements, such as handling large data sets, complex workflows, and specific collaboration needs that GitHub may not fully meet. This has led to the emergence of alternative platforms, each offering distinctive features and benefits.
In this blog, we explore the top five GitHub alternatives that are particularly suitable for data science projects, providing various options for collaboration, project management, and data and model management.
kaggle is recognized in the data science community for its unique combination of data science competencies, data sets, and collaborative environment.
The platform offers access to a vast repository of data sets and an opportunity for data scientists to test their skills in real-world scenarios through competitions. Additionally, I provide access to edit, run and share code notebooks with results.
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I’ve been using Kaggle for three years and I love it. This platform allows me to quickly run deep learning projects on free GPUs and TPUs. With their help, I was able to build a strong portfolio by sharing my analytics reports and machine learning projects. Additionally, I have participated in various data analysis and machine learning competitions, which has helped me improve my skills in these areas. Overall, Kaggle has been an excellent resource that has allowed me to grow both personally and professionally.
If you are a beginner in data science, I recommend starting with Kaggle instead of GitHub. Kaggle offers a wide range of free features that are essential for any data science project. Plus, you can learn from others and ask questions directly in a community of like-minded people who want to help each other.
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hugging face has quickly become a hub for the latest advances in natural language processing (NLP) and machine learning. It distinguishes itself by offering a wide collection of pre-trained models, along with a collaborative ecosystem to train and share new models. Plus, it’s now easy to upload your dataset and deploy your machine learning web application 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, create a demo with the model, or create an inference. Plus, you can now comment and submit pull requests, just like on GitHub.
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I use Hugging Face frequently to deploy models, load trained models, and build a robust machine learning portfolio. I have implemented deep reinforcement learning, multilingual speech recognition, and large language models.
This platform is designed primarily 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 latest generation model, you can even request paid features. This makes it the go-to platform for anyone aspiring to become a machine learning engineer or NLP engineer.
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DagsHub is a platform tailored 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 only code but also data sets and machine learning models, addressing a common challenge in the field.
The platform integrates well with popular data science tools, allowing for a seamless transition from other environments. DagsHub’s standout feature is its community aspect, offering a space for data scientists to collaborate and share knowledge, making it a particularly attractive option for those looking to interact with a community of peers.
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I’m a big fan of DagsHub because of its easy-to-use approach to uploading and accessing data and models. DagsHub provides a simple API and GUI that allows you to upload and access data and models with ease. Additionally, it provides MLFlow instances for experiment tracking and model registration. Additionally, it provides a free instance of Label Studio to label your data. It is 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.
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GitLab It is a good alternative to GitHub for all types of technology professionals. It offers robust collaboration and version control, CI/CD, project management and issue tracking, security and compliance, analytics and insights, webhooks and REST APIs, pages, and more.
This platform is an ideal solution for developers and data scientists who need to create 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.
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I’ve been using GitLab for the past three years, mainly to get familiar with the platform and migrate my static websites from GitHub to GitLab. GitLab’s user interface is easy to understand and offers a wide range of tools for free users. Plus, you have the option of hosting your own. GitLab Community Edition instance free, giving you full control over your projects.
Like GitHub, GitLab can also be used as a portfolio for your data science projects. You can upload and share all your work in one place, and it even has better collaboration tools for larger, more complex projects. GitLab is a powerful platform that you should definitely consider, even if you are already happy with GitHub.
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Codeberg.org distinguishes itself as a community-driven, nonprofit platform that places a strong emphasis on open source and privacy. It offers a simple and easy-to-use interface that appeals to those looking for a simple and straightforward code hosting solution. For data scientists who prioritize open source values and data privacy, Codeberg presents an attractive alternative.
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It offers CI/CD, Pages, SSH and GPG solutions, 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 trying them to host your projects.
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Each of these platforms offers unique features and benefits for data scientists. GitLab excels at integrated workflow management, DagsHub and Hugging Face are designed 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 advanced project management, community engagement, specialized tools, or commitment to open source principles, data scientists may find a suitable alternative to GitHub among these options.
Abid Ali Awan (@1abidaliawan) is a certified professional data scientist who loves building machine learning models. Currently, he focuses on content creation and writing technical blogs on data science and machine learning technologies. Abid has a Master’s degree in technology Management and a Bachelor’s degree in Telecommunications Engineering. His vision is to build an artificial intelligence product using a graph neural network for students struggling with mental illness.
