Popular GitHub Repositories for Mlops Marathon 2023

A high-throughput and memory-efficient inference and serving engine for LLMs

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

Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/

?? Build multimodal AI applications with cloud-native stack

CVPR 2025 论文和开源项目合集

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

Turns Data and AI algorithms into production-ready web applications in no time.

Machine Learning Engineering Open Book

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database?.

A curated list of references for MLOps

Free MLOps course from DataTalks.Club

[CVPR 2023] SadTalker：Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation

text and image to video generation: CogVideoX (2024) and CogVideo (ICLR 2023)

Run any open-source LLMs, such as DeepSeek and Llama, as OpenAI compatible API endpoint in the cloud.

Example ? Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using ? Amazon SageMaker.

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

This repository delivers end-to-end, code-first tutorials covering every layer of production-grade GenAI agents, guiding you from spark to scale with proven patterns and reusable blueprints for real-world launches.

A booklet on machine learning systems design with exercises. NOT the repo for the book "Designing Machine Learning Systems"

Build, Manage and Deploy AI/ML Systems