The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.

🌀 𝗧𝗵𝗲 𝗙𝘂𝗹𝗹 𝗦𝘁𝗮𝗰𝗸 𝟳-𝗦𝘁𝗲𝗽𝘀 𝗠𝗟𝗢𝗽𝘀 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 | 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟𝗘 & 𝗠𝗟𝗢𝗽𝘀 for free by designing, building and deploying an end-to-end ML batch system ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 2.5 𝘩𝘰𝘶𝘳𝘴 𝘰𝘧 𝘳𝘦𝘢𝘥𝘪𝘯𝘨 & 𝘷𝘪𝘥𝘦𝘰 𝘮𝘢𝘵𝘦𝘳𝘪𝘢𝘭𝘴

Distributed version-control for geospatial and tabular data

Metadata store for Production ML

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

A demonstration of how DVC and MLFlow can be used in the task of data relabeling

Python Data as Code core implementation

A CKAN extension for data versioning.

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

Automatic data change tracking for SQLAlchemy

Automatic data change tracking for Django

Kedro-Dolt Hook Plugin

Newron is a data-centric ML platform to easily build, manage, deploy and continuously improve models through data driven development.

Production-grade MLOps: Model deployment, monitoring, feature stores, and ML pipelines for real-world AI systems.

This project demonstrates a complete workflow for analyzing sales data with missing values. It includes data cleaning, feature engineering, aggregation, and visualizations using Python libraries such as Pandas, NumPy, and Matplotlib.

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.

Production-ready ML pipeline solving reproducibility challenges with DVC, docker, MLflow & DagHub

Simple lightweight dataset versioning utility based purely on the file system and symbolic links.

Centralized Feature Store built on DVC for ML feature versioning, validation, and sharing. Includes MLflow integration for experiment tracking and Kubeflow Pipeline components for production ML workflows.

Here it is shown that how we can do versioning of our data using DVC while the remote location will be in dagshub.