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What is DVC?

DVC, or Data Version Control, is an open-source tool that helps manage and version control data, models, and experiments in data science and machine learning projects.

How does DVC work?

DVC works by integrating with existing Git repositories, allowing users to track changes in data and models, manage large files, and create reproducible workflows using GitOps principles.

What are the primary use cases for DVC?

DVC is primarily used for organizing data and models in machine learning projects, facilitating experiment tracking, and managing datasets that are large and complex.

Is DVC free to use?

Yes, DVC is free and open-source, ensuring that users can utilize it without any cost for their data science and machine learning projects.

Can DVC handle unstructured data?

Yes, DVC can manage unstructured data, and it provides tools like DataChain to curate and process such data using local machine learning models.

What platforms does DVC support?

DVC supports multiple platforms, including macOS, Windows, and Linux, and can be installed using package managers like pip, conda, and brew.

Q: How often does Dvc experience outages?

A: Major outages are rare, occurring only a few times a year. Minor issues can happen more frequently.

Q: How long do Dvc outages typically last?

A: Most outages are resolved within a few hours. Extended outages lasting more than a day are extremely rare.