Stop programming common dvc stages. Configure them.
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Updated
Jul 5, 2023 - Python
Stop programming common dvc stages. Configure them.
practice about data_version_control(DVC)
useR! 2022 talk
Personal project aimed at developing a ML service which resembles a production environment system
Using DVC for Data Versioning
Playground for learning DVC
In this repository, an ML-Ops task is undertaken to practice configuring and storing data using DVC on GitHub. The goal is to explore how DVC seamlessly integrates for efficient data management, enhancing reproducibility and scalability in machine learning workflows.
Demonstration about how to use DVC(Data Version Control)
Deploying a Machine Learning Model on Heroku with FastAPI using CI/CD tools as GitHub Actions and Heroku Automatic Deployment.
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.
Declaratively create, transform, manage and version ML datasets.
A machine learning pipeline taking you from raw data to fully trained machine learning model - from data to model (d2m).
Lesson 2 tutorial: Versioning Data and Model for the ML REPA School course: Machine Learning experiments reproducibility and engineering with DVC
An abstraction layer for data storage systems
Python Data as Code core implementation
Deprecated. See https://github.com/datopian/ckanext-versions. ⏰ CKAN extension providing data versioning (metadata and files) based on git and github.
A JSON-based format for working with machine learning data, with a focus on data interoperability.
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