Popular GitHub Repositories for Neptune

Python toolkit for building GraphRAG applications

Flexible and scalable template based on PyTorch Lightning + Hydra. Efficient workflow and reproducibility for rapid ML experiments.

Easiest way of fine-tuning HuggingFace video classification models

Lightweight graph database with relational and vector support built using Sqlite, designed to power knowledge and artificial intelligence persistence and retrieval.

Automated question generation and question answering from Turkish texts using text-to-text transformers

Uncovering Hidden Connections in Unstructured Financial Data using Amazon Bedrock and Amazon Neptune

Neptune - MLflow integration ? Experiment tracking with advanced UI, collaborative features, and user access management.

This Guidance demonstrates how to create an intelligent manufacturing digital thread through a combination of knowledge graph and generative artificial intelligence (AI) technologies. A digital thread offers an integrated approach to combine disparate data sources across enterprise systems, increasing traceability, accessibility, collaboration.

Music Modeling Kit

Sales Conversion Optimization MLOps: Boost revenue with AI-powered insights. Features H2O AutoML, ZenML pipelines, Neptune.ai tracking, data validation, drift analysis, CI/CD, Streamlit app, Docker, and GitHub Actions. Includes e-mail alerts, Discord/Slack integration, and SHAP interpretability. Streamline ML workflow and enhance sales performance.

Code for NePTuNe: Neural Powered Tucker Network for Knowledge Graph Completion

This repository contains sample code for ML on graph use cases using Amazon Neptune ML

Neptune - TensorBoard integration ? Experiment tracking with advanced UI, collaborative features, and user access management.

pip install the deep learning & HPC starter pack to begin your project.

This repository contains a series of 4 jupyter notebooks demonstrating how AWS AI Services like Amazon Rekognition, Amazon Transcribe and Amazon Comprehend can help you extract valuable metadata from your video assets and store that information in a Graph database like Amazon Neptune for maximum query performance and flexibility.

This repo implements a minimalistic pytorch_lightning + neptune + torchserve flow for (computer vision) model training and deployment

Experiment tracking and model registry in the time series forecasting project

Neptune在不同应用场景和多种MLOps工具下的使用方法示例

Train and build a sentiment model using pytorch for fitness apps using Bert, dockerized and container deployed to the cloud(AWS)

Boilerplate to quickly iterate on projects with PyTorch Lightning, Neptune.ai & Optuna