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Dataset & Code Versioning Essentials with DagsHub | Master the essentials of data and code versioning for machine learning projects with DagsHub. Ensure the reproducibility of your ML experiments with traceable and collaborative workflows. Ideal for data scientists seeking reliable version control. |
Versioning your datasets and code is a critical component of data science projects that ensures the reproducibility of ML experiments. It provides traceability and enables collaboration among team members with ease.
Let's learn when, and how to version your datasets with DagsHub Data Engine, your data files using DVC, and code using Git, and how to manage and host all these components on DagsHub.
Versioning makes sense in cases where your datasets might change, and you want to keep track of those changes.
This might be done for various reasons for example:
In most ML projects, it is recommended to version your data.
There are 3 main data change scenarios relevant for versioning:
In all cases, versioning code is required.
Select what type of versioning you'd like to dive into:
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Are you sure you want to delete this access key?
Are you sure you want to delete this access key?
Are you sure you want to delete this access key?
Are you sure you want to delete this access key?