🦉 Data Versioning and ML Experiments

sgr (command line client for Splitgraph) and the splitgraph Python library

Create, visualize, run & benchmark DVC pipelines in Python & Jupyter notebooks.

Python framework for artificial text detection: NLP approaches to compare natural text against generated by neural networks.

Python Data as Code core implementation

create a robust, simple, effecient, and modern end to end ML Batch Serving Pipeline Using set of modern open-source/free Platforms/Tools

A CKAN extension for data versioning.

MULLER: A Multimodal Data Lake Format for Collaborative AI Data Workflows

A machine learning pipeline taking you from raw data to fully trained machine learning model - from data to model (d2m).

Deprecated. See https://github.com/datopian/ckanext-versions. ⏰ CKAN extension providing data versioning (metadata and files) based on git and github.

An abstraction layer for data storage systems

Data version control with Makefile and DVC for a regression task to estimate insurance costs for certain individuals.

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.

DVC & Git Integration Demo | Một repository mẫu hướng dẫn cách quản lý dự án Machine Learning: Sử dụng Git để quản lý mã nguồn và DVC để track dữ liệu/mô hình. Bao gồm Pipeline tự động (DVC Stages) và quản lý thử nghiệm (Experiment Tracking).

Projeto prático de MLOps focado no ciclo de vida completo de modelos de ML: automação de pipelines, gerenciamento de artefatos, engenharia de features e disponibilização de modelos como serviço (Model-as-a-Service).

Personal project aimed at developing a ML service which resembles a production environment system

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.

Deploying a ML Model to Cloud Platform with FastAPI applying CI/CD practices

Stop programming common dvc stages. Configure them.

A complete MLOps pipeline for fake news detection using DVC for data versioning and MLflow for experiment tracking. Features Logistic Regression and Naive Bayes models, Flask web interface, and comprehensive evaluation metrics.