practice about data_version_control(DVC)
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Updated
Feb 12, 2020
practice about data_version_control(DVC)
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.
useR! 2022 talk
A JSON-based format for working with machine learning data, with a focus on data interoperability.
SageMaker Experiments and DVC
Personal project aimed at developing a ML service which resembles a production environment system
Meta data server & client tools for game development
A curated list to help you manage temporal data across many modalities 🚀.
An abstraction layer for data storage systems
Demonstration about how to use DVC(Data Version Control)
Metadata management in Go
Lesson 2 tutorial: Versioning Data and Model for the ML REPA School course: Machine Learning experiments reproducibility and engineering with DVC
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.
Git-like data versioning.
Python framework for artificial text detection: NLP approaches to compare natural text against generated by neural networks.
Declaratively create, transform, manage and version ML datasets.
Playground for learning DVC
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