awesome-ml-sys-tutorial
A personal collection of ML systems learning notes, focused on RL infrastructure, inference systems, and AI infrastructure fundamentals for readers studying how these systems work.
Share on XLicense: Apache-2.0
Overview
This repository collects the author's learning notes and blog posts on machine-learning systems, with a particular focus on reinforcement-learning infrastructure, online and offline inference systems, and AI infrastructure fundamentals. It gives readers a written path into implementation details and questions around the reliability of systems underlying research conclusions. The index includes material on topics such as omni-model inference and SGLang memory behavior, with English and Chinese versions linked for some articles. Use it as a technical study resource, not a software package.
Key features
- Learning notes on reinforcement-learning infrastructure
- Articles about online and offline inference systems
- Coverage of AI infrastructure fundamentals, including omni-model inference and SGLang memory behavior
Best for
Researchers and engineers learning about ML systems or examining how infrastructure can affect research conclusions can use these notes as a study resource.
- Upstream
- zhaochenyang20/Awesome-ML-SYS-Tutorial
- Fork on GitHub
- Guo-astro/awesome-ml-sys-tutorial
- Upstream stars
- 7.6k
- Category
- Learning, interviews and curated lists
- Language
- HTML
- License
- Apache-2.0
- Forked
- 2026-10-17
- Sync status
- In syncLast synced 2026-10-17
More in Learning, interviews and curated lists
A study guide for learning 30 foundational AI papers through hands-on implementations, notes, and exercises. It helps readers connect paper ideas to working deep-learning concepts one paper at a time.
Forked 2026-10-16Last synced 2026-10-17Custom license (see repository)Learning, interviews and curated listsGitHub
A system design learning repository that gathers case studies, interview resources, a design template, and guidance for studying and practicing system design.
Forked 2026-10-14Last synced 2026-10-17License: MITLearning, interviews and curated listsGitHub
A concise introduction to reinforcement learning, from fundamentals to applied algorithms. Use the book to learn the subject and its PyTorch examples and supplementary proofs to study key methods in more detail.
Forked 2026-10-14Last synced 2026-10-17No license declaredLearning, interviews and curated listsGitHub
A curated directory of Telegram resources for discovering bots, groups, channels, tools, and development libraries. Use it to find services and references across the Telegram ecosystem.
Forked 2026-10-12Last synced 2026-10-17No license declaredLearning, interviews and curated listsGitHub