M

mlops

🔧🔗https://github.com/vllm-project/vllm

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

🔧🔗https://github.com/tensorzero/tensorzero TensorZero is an open-source stack for industrial-grade LLM applications. It unifies an LLM gateway, observability, optimization, evaluation, and experimentation

🔧🔗https://github.com/vllm-project/vllm-ascend Community maintained hardware plugin for vLLM on Ascend

Build applications that make decisions (chatbots, agents, simulations, etc...). Monitor, persist, and execute on your own infrastructure. burr.dagworks.io

Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage and metadata. Runs and scales everywhere python does.

🔗 https://github.com/Avaiga/taipy 🔗 https://www.taipy.io Turns Data and AI algorithms into production-ready web applications in no time.

🔧🔗https://github.com/pytorch/serve

Serve, optimize and scale PyTorch models in production

🔧🔗https://github.com/HumanSignal/label-studio

Label Studio is a multi-type data labeling and annotation tool with standardized output format

🔧🔗https://github.com/decodingml/personalized-recommender-course 👕 Open-source course on architecting, building and deploying a real-time personalized recommender for H&M fashion arti

🤖 𝗟𝗲𝗮𝗿𝗻 for 𝗳𝗿𝗲𝗲 how to 𝗯𝘂𝗶𝗹𝗱 an end-to-end 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗟𝗟𝗠 & 𝗥𝗔𝗚 𝘀𝘆𝘀𝘁𝗲𝗺 using 𝗟𝗟𝗠𝗢𝗽𝘀 best practices: ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 11 𝘩𝘢𝘯𝘥𝘴-𝘰𝘯 𝘭𝘦𝘴𝘴𝘰𝘯𝘴 https://github.com/decodingml/llm-twin-course

https://github.com/Upsonic/On-Prem Self-Driven Autonomous Python Libraries

💻 Decoding ML articles hub: Hands-on articles with code on production-grade ML

https://github.com/decodingml/articles-code

https://github.com/Upsonic/Server Self-Driven Autonomous Python Libraries

https://github.com/dot-agent/nextpy 🤖Self-Modifying Framework from the Future 🔮 World's First AMS