DVC
DVC is an open-source version control system for machine learning projects. It helps track datasets, metrics, parameters and models to improve reproducibility and collaboration.
version-control
reproducibility
collaboration
DVC: Open-Source Version Control for Machine Learning Projects
DVC is an open-source version control system for machine learning projects. It helps track datasets, metrics, parameters and models to improve reproducibility and collaboration.
What is DVC?
DVC is an open-source version control system designed for machine learning and data science projects. It integrates with Git to improve version control of large files and data sets.
Some key features of DVC include:
- Dataset and model versioning - DVC tracks changes to data sets and ML models, enabling experiment annotation and comparison between versions.
- Data registries - Remote storage options to store large data files outside the Git repository like Amazon S3, Azure Blob Storage, Google Drive etc.
- Metrics tracking - Auto-generated records of metric values for each commit to track progress.
- Pipelines - Helps codify, organize and structure ML workflows from data processing to model evaluation steps.
- Experiment tracking - Visualize experiments with parameters to compare performance.
- Git integration - Seamless usage alongside Git, handling large files that Git would struggle with.
DVC makes life easier for data scientists and ML engineers by automating pipeline execution, enabling reproducibility and helping collaborate with others more efficiently on machine learning projects.
DVC Features
Features
- Version control for machine learning models and datasets
- Model registry to organize experiments
- Metrics tracking to monitor performance
- Compare experiments through git branches and tags
- Share experiments through remote storage (S3, GCS, etc)
Pricing
- Open Source
Pros
Lightweight and framework agnostic
Integrates with existing workflows
Open source and free
Improves reproducibility
Enables collaboration
Cons
Limited adoption so far
Less features than paid MLOps tools
Steep learning curve for Git workflows
Official Links
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