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

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.

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

🔧🔗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