🦉 ML Experiments and Data Management with Git
-
Updated
May 13, 2024 - Python
🦉 ML Experiments and Data Management with Git
Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai
Dolt – Git for Data
lakeFS - Data version control for your data lake | Git for data
Quilt is a data mesh for connecting people with actionable data
sgr (command line client for Splitgraph) and the splitgraph Python library
Meta data server & client tools for game development
Metrics Observability & Troubleshooting
A CKAN extension for data versioning.
Lesson 2 tutorial: Versioning Data and Model for the ML REPA School course: Machine Learning experiments reproducibility and engineering with DVC
Deprecated. See https://github.com/datopian/ckanext-versions. ⏰ CKAN extension providing data versioning (metadata and files) based on git and github.
Create, visualize, run & benchmark DVC pipelines in Python & Jupyter notebooks.
An Git-like version control file system for data lineage & data collaboration.
SageMaker Experiments and DVC
Deploying a Machine Learning Model on Heroku with FastAPI using CI/CD tools as GitHub Actions and Heroku Automatic Deployment.
A curated list to help you manage temporal data across many modalities 🚀.
An abstraction layer for data storage systems
create a robust, simple, effecient, and modern end to end ML Batch Serving Pipeline Using set of modern open-source/free Platforms/Tools
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}.
Add a description, image, and links to the data-version-control topic page so that developers can more easily learn about it.
To associate your repository with the data-version-control topic, visit your repo's landing page and select "manage topics."