dvc

DVC (Data Version Control) is an open-source command-line tool that helps data scientists and machine learning engineers manage and version control their data and machine learning models. It is designed to work alongside Git and is often referred to as "Git for data."

Here are some key features and functionalities of DVC:

1. Data and model versioning: DVC allows you to track, version, and share your data and ML models. It integrates seamlessly with Git, ensuring that data and model files are efficiently managed.

2. Reproducible pipelines: DVC allows you to define and run complex data pipelines, making it easy to reproduce experiments and workflows. You can track dependencies between steps, manage execution order, and cache intermediate outputs.

3. Large file handling: DVC is built to handle large data files efficiently. It provides a mechanism for data file storage and handling, known as DVC remotes, which supports various storage options like local file systems, cloud storage services, and networked storage.

4. Collaboration: DVC enables seamless collaboration between team members by providing a shared data repository and facilitating sharing and syncing of data and ML models. It helps manage and merge changes made by different team members across a project.

5. Data versioning in the cloud: DVC integrates with popular cloud services like AWS S3, Google Cloud Storage, and Microsoft Azure Blob Storage, allowing you to store data and models in the cloud and version them.

6. Experiment tracking: DVC provides tools to track experiments, including logging and comparing metrics, hyperparameters, and output visualizations, giving you insights into the performance of different models and experiments.

Overall, DVC simplifies the management, versioning, and collaboration of data and ML models, ensuring reproducibility and scalability in data science projects.