Collecting thoughts about data versioning
-
Updated
Jul 3, 2019
Collecting thoughts about data versioning
practice about data_version_control(DVC)
Verta ai ModelDB on AWS Cloud with integration into Amazon SageMaker for ML training data versioning and experiment tracking
Deprecated. See https://github.com/datopian/ckanext-versions. ⏰ CKAN extension providing data versioning (metadata and files) based on git and github.
Deploying a Machine Learning Model on Heroku with FastAPI using CI/CD tools as GitHub Actions and Heroku Automatic Deployment.
Learning data and model versioning with ClearML while cleaning and modeling happiness by country with a Kaggle dataset
DVC + MLflow for data monitoring and ML lifecycle management
A JSON-based format for working with machine learning data, with a focus on data interoperability.
Metadata store for Production ML
Newron is a data-centric ML platform to easily build, manage, deploy and continuously improve models through data driven development.
Project with tabular data versioned with Artifacts.
A curated list to help you manage temporal data across many modalities 🚀.
A versioning data store for time-variant graph data.
Repository for evaluating the different approaches to data versioning
Articles, tutorials, and tools about creating scalable and sustainable ML/DL systems.
following best practices to productionize an ML project
A CKAN extension for data versioning.
The provided demo project demonstrates the practical implementation and advantages of using DVC. It showcases how DVC simplifies data versioning and model versioning while working in tandem with Git to create a cohesive version control system tailored for data science projects.
Using DVC for Data Versioning
Add a description, image, and links to the data-versioning topic page so that developers can more easily learn about it.
To associate your repository with the data-versioning topic, visit your repo's landing page and select "manage topics."