DVC

DVC (Data Version Control) is an open-source, Git-based data science tool designed to enable version control for machine learning development. According to the vendor, DVC provides a solution for tracking changes, collaborating, and reproducing experiments in data science projects. The product is said to cater to businesses of various sizes, from startups to Fortune 500 companies. It is widely adopted by professionals in fields such as data science, machine learning engineering, data engineering, research science, and software engineering. DVC is used in industries including technology, healthcare, finance, retail, and manufacturing.

Key Features

Data Version Control: According to the vendor, DVC allows users to version control machine learning models, data sets, and intermediate files. It connects data and models with code and supports various storage types for remote caching of large files. The vendor claims that DVC enables reproducibility and easy switching between experiments.

ML Experiment Management: The vendor states that DVC allows users to try different ideas and compare results using Git branches. It offers automatic metric-tracking for easy navigation and comparison. The vendor claims that DVC enables simple and fast branching, even with large data file sizes. It also provides a cleaner project structure and faster iterations with intermediate artifact caching.

Deployment & Collaboration: According to the vendor, DVC allows users to move ML models, data, and code into production or remote machines using push/pull commands. It treats lightweight pipelines as a first-class citizen mechanism in Git and offers language-agnostic pipelines that connect multiple steps into a DAG. The vendor claims that DVC facilitates easy code deployment into production environments.

Track Experiments in Git: The vendor states that DVC provides full code and data provenance for tracking the complete evolution of ML models. It allows users to compare results and restore entire experiment states. According to the vendor, DVC enables collaboration and result sharing across teams. It also facilitates experiment reproducibility and can be used as baselines for new iterations.

Data Management: According to the vendor, DVC allows users to track and version large amounts of data along with code. It supports building reproducible, data-driven pipelines and can be used as a build system for managing data sets. The vendor claims that DVC seamlessly integrates with Git, enabling collaboration in data science projects.

Experiment Tracking: The vendor states that DVC enables users to easily track experiments and their progress. It allows users to instrument code for tracking without changing their workflow. According to the vendor, DVC facilitates collaboration on ML experiments, similar to how software engineers collaborate on code. It ensures that all files and metrics are consistent and in the right place for reproducibility.

CI/CD for Machine Learning: According to the vendor, DVC integrates with continuous integration and delivery (CI/CD) pipelines. It automates testing, building, and deploying ML models. The vendor claims that DVC ensures consistent and reliable ML model deployments and streamlines the process of delivering ML models into production.

Fast and Secure Data Caching Hub: The vendor states that DVC can be used as a remote cache for large files. It accelerates data access and sharing across teams, supporting various storage types for flexibility. According to the vendor, DVC ensures data security and integrity.

Model Registry: According to the vendor, DVC provides a centralized registry for managing ML models. It allows users to track model versions, metadata, and performance metrics. The vendor claims that DVC simplifies sharing and deployment of models across teams, streamlining model management and collaboration.

Data Registry: The vendor states that DVC offers a centralized registry for managing data sets. It allows users to track data versions, metadata, and usage history. According to the vendor, DVC facilitates sharing and collaboration on data sets across teams, ensuring data consistency and reproducibility.