Popular GitHub Repositories for Quality Requirements

Learn how to design, develop, deploy and iterate on production-grade ML applications.

? Papers & tech blogs by companies sharing their work on data science & machine learning in production.

Composio equip's your AI agents & LLMs with 100+ high-quality integrations via function calling

The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

PyTorch implementation of the U-Net for image semantic segmentation with high quality images

Build high-quality LLM apps - from prototyping, testing to production deployment and monitoring.

Refine high-quality datasets and visual AI models

High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)

High-quality multi-lingual text-to-speech library by MyShell.ai. Support English, Spanish, French, Chinese, Japanese and Korean.

Evidently is ??an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.

The Open Source Feature Store for AI/ML

MuseTalk: Real-Time High Quality Lip Synchorization with Latent Space Inpainting

VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

A distributed approximate nearest neighborhood search (ANN) library which provides a high quality vector index build, search and distributed online serving toolkits for large scale vector search scenario.

Argilla is a collaboration tool for AI engineers and domain experts to build high-quality datasets

Pre-trained Deep Learning models and demos (high quality and extremely fast)

StarVector is a foundation model for SVG generation that transforms vectorization into a code generation task. Using a vision-language modeling architecture, StarVector processes both visual and textual inputs to produce high-quality SVG code with remarkable precision.

Learn how to design, develop, deploy and iterate on production-grade ML applications.

LLaMA-Omni is a low-latency and high-quality end-to-end speech interaction model built upon Llama-3.1-8B-Instruct, aiming to achieve speech capabilities at the GPT-4o level.

[CVPR 2024] Official PyTorch implementation of SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering