model-versioning
There are 30 repositories under model-versioning topic.
wandb/wandb
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
SwanHubX/SwanLab
⚡️SwanLab - an open-source, modern-design AI training tracking and visualization tool. Supports Cloud / Self-hosted use. Integrated with PyTorch / Transformers / LLaMA Factory / veRL/ Swift / Ultralytics / MMEngine / Keras etc.
VertaAI/modeldb
Open Source ML Model Versioning, Metadata, and Experiment Management
overtrue/laravel-versionable
⏱️Make Laravel model versionable
kleveross/ormb
Docker for Your ML/DL Models Based on OCI Artifacts
westonganger/active_snapshot
Simplified snapshots and restoration for ActiveRecord models and associations with a transparent white-box implementation
FederatedAI/FATE-Serving
A scalable, high-performance serving system for federated learning models
layerai-archive/sdk
Metadata store for Production ML
kleveross/klever-model-registry
Cloud Native Machine Learning Model Registry
rstudio/vetiver-python
Version, share, deploy, and monitor models.
laravel-enso/history-tracker
Laravel Model history tracking made easy
beringresearch/lab
A lightweight command line interface for the management of arbitrary machine learning tasks
latomate07/Laraversion
A Laravel package for versioning Eloquent models with a simple and scalable approach.
hyper-ml/hyperML
Frictionless Machine Learning on Kubernetes
IPVS-AS/MMP-Frontend
A Model Management Platform (MMP) for Industry 4.0 Environments (Frontend)
IPVS-AS/MMP-Backend
A Model Management Platform (MMP) for Industry 4.0 Environments (Backend)
hectorLop/Wandb-MV
Model versioning using Weight&Biases with Python.
NewronAI/newron-sdk
Newron is a data-centric ML platform to easily build, manage, deploy and continuously improve models through data driven development.
VineetKT/ML_fastapi_on_Heroku_CI-CD
Deploying a Machine Learning Model on Heroku with FastAPI using CI/CD tools as GitHub Actions and Heroku Automatic Deployment.
Md-Emon-Hasan/MLFlow-Basic
Covers essential features like model tracking, versioning, and experiment, providing a foundation for efficient ML project lifecycle management.
tahonick/MLOps-Data-versioning-with-ClearML
Learning data and model versioning with ClearML while cleaning and modeling happiness by country with a Kaggle dataset
averagepythonfan/mongomv
Framework for model versioning with MongoDB
christopheitenberger/VSOL
Versioning System for Online Learning systems (VSOL)
hectorLop/mixver
Custom versioning of Machine Learning models
kstarkiller/moviewise_recommender_system
A study of a recommendation system for movies used as a first step in ML Flow
MarionChaff/model-storage-manager
A simple class to handle model update, saving and loading with GCP
BodieCoding/ml-project-template
A template for building governed and reproducible machine learning projects, enabling transparent tracking of data, models, and deployments across various platforms.
Faisal-AlDhuwayhi/Deploying-ML-Model-to-Cloud
Deploying a ML Model to Cloud Platform with FastAPI applying CI/CD practices
SayamAlt/Airline-Passenger-Satisfaction-Classification
Successfully developed a machine learning model to predict Airline Passenger Satisfaction by building an end-to-end MLOps pipeline. It integrates DVC for data versioning, a Dockerfile for containerization, and CI/CD using GitHub Actions for automated deployment.