There are 22 repositories under data-version-control topic.
Metrics Observability & Troubleshooting
sgr (command line client for Splitgraph) and the splitgraph Python library
A curated list to help you manage temporal data across many modalities 🚀.
Git based Version Control File System for joint management of code, data, model and their relationship.
Data version control for reproducible analysis pipelines in R with {targets}.
Meta data server & client tools for game development
Mount remote repositories, models and datasets managed by Git LFS instantly.
SageMaker Experiments and DVC
Python framework for artificial text detection: NLP approaches to compare natural text against generated by neural networks.
create a robust, simple, effecient, and modern end to end ML Batch Serving Pipeline Using set of modern open-source/free Platforms/Tools
Python Data as Code core implementation
A CKAN extension for data versioning.
Metadata management in Go
Deprecated. See https://github.com/datopian/ckanext-versions. ⏰ CKAN extension providing data versioning (metadata and files) based on git and github.
A machine learning pipeline taking you from raw data to fully trained machine learning model - from data to model (d2m).
Data version control with Makefile and DVC for a regression task to estimate insurance costs for certain individuals.
An abstraction layer for data storage systems
Lesson 2 tutorial: Versioning Data and Model for the ML REPA School course: Machine Learning experiments reproducibility and engineering with 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.
Demonstration about how to use DVC(Data Version Control)
ML Project Template containing data, data collection, feature engineering, model trainings, model config files, tests, and serialized models.
Deploying a Machine Learning Model on Heroku with FastAPI using CI/CD tools as GitHub Actions and Heroku Automatic Deployment.
An end-to-end MLOps pipeline for emotion detection. Features data versioning with DVC + AWS S3, model training and evaluation with MLflow, CI/CD via GitHub Actions, FastAPI serving, Docker containerization, AWS EC2 deployment, and experiment tracking on DagsHub.
The Chicken Disease Classification Using MLOps DVC Pipeline project utilizes the VGG16 architecture to analyze images of chicken fecal matter, enabling early disease detection and reducing economic losses in poultry farming.
📂 Comprehensive guide on using DVC for efficient and reproducible machine learning projects, covering essential commands and workflows.
This repository contains a complete machine learning pipeline for Speech Emotion Recognition (SER) using Deep Neural Networks (DNNs).
Deploying a ML Model to Cloud Platform with FastAPI applying CI/CD practices
A full-stack machine learning architecture for food delivery ETA prediction, leveraging a DVC-driven pipeline, automated CI/CD workflows, cloud artifact management, and LGBM-based stacked regression ensemble for high-fidelity time estimations.