Version Control

Version control plays a crucial role in overseeing and documenting alterations made to data, code, and models within AI and data science projects. 

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Complex Projects Require Consistency

Manage code, data, and models

Integration with version control systems like Git enable change tracking and easy collaboration.

Manage environments

Conda environments ensure reproducible configurations of packages and libraries. 

Collaborate effectively

Multiple users can work on the same project and quickly resolve conflicting changes.

Reproduce results

Integrated tools like Jupyter Notebooks enable users to document their code, methodologies, assumptions, and results, facilitating reproducibility and transparency.

Connect to git repos

Users can also connect to external git repos to use specific git extensions.

Data Science & AI Workbench 

Workbench helps you to manage your project’s evolution with robust version control capabilities for streamlined collaboration and reproducibility. 

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Notebooks and Jupyter: Best Practices

Learn how to set up reproducible environments.

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Saving and committing changes in a project

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Connecting to an external version control repository

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Learn how version control enhances project efficiency. Talk to an expert today.