practice about data_version_control(DVC)
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Updated
Feb 12, 2020
practice about data_version_control(DVC)
Stop programming common dvc stages. Configure them.
useR! 2022 talk
Personal project aimed at developing a ML service which resembles a production environment system
Using DVC for Data Versioning
Demonstration about how to use DVC(Data Version Control)
Playground for learning DVC
Declaratively create, transform, manage and version ML datasets.
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 Data as Code core implementation
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.
A JSON-based format for working with machine learning data, with a focus on data interoperability.
Metadata management in Go
A machine learning pipeline taking you from raw data to fully trained machine learning model - from data to model (d2m).
Data version control for reproducible analysis pipelines in R with {targets}.
Python framework for artificial text detection: NLP approaches to compare natural text against generated by neural networks.
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