Introduction to Awesome-System-for-Machine-Learning
The Awesome-System-for-Machine-Learning is a comprehensive compilation of resources and research focusing on systems specifically designed for machine learning (ML), large language models (LLMs), and generative AI (GenAI). This project is continually maintained and welcomes contributions from the community, making it an evolving repository for those interested in ML systems.
Project Overview
At the heart of this initiative is a curated list of pivotal research, categorized and linked to their corresponding code wherever available. The aim is to provide both foundational and cutting-edge insights into the infrastructure supporting machine learning and associated domains.
Infrastructure Categories
- ML / DL Infrastructure: This includes resources for data processing, training systems, inference systems, and overall machine learning infrastructure.
- LLM Infrastructure: Covers aspects related to the training and serving of large language models.
- Domain-Specific Infrastructure: Encompasses systems for video processing, AutoML, edge AI, GNN, federated learning, and deep reinforcement learning.
Conferences for ML/LLM Systems
The repository showcases influential conferences like OSDI, SOSP, SIGCOMM, NSDI, and MLSys, highlighting venues where groundbreaking research in ML systems is often presented.
General Resources
Surveys and Papers
A section is dedicated to surveys and papers that dive deep into various aspects of ML systems, offering an extensive list ranging from cloud ML systems to the hidden technical debt in these complex infrastructures.
Books
Highlighted are authoritative books on computer architecture, distributed machine learning, and systems for AI, providing invaluable resources for deeper learning.
Videos
A rich collection of video resources is available, including keynotes and talks from noted figures such as Jeff Dean and conferences like ICML, promoting a visual and auditory learning experience.
Courses
Links to courses from prestigious universities like Stanford, UC Berkeley, and CMU offer structured learning paths, covering topics from AI systems design to large dataset machine learning.
Blogs
A plethora of blog posts and articles guide readers through practical applications, deployment strategies, and innovations in ML systems. These resources serve both as instructional content and as a window into current trends and techniques.
Encourages Community Contribution
The project is open-source, hosted on GitHub with ongoing maintenance, inviting contributions from the global community. Researchers, developers, and enthusiasts from diverse backgrounds can contribute to its growth, enhancing their own expertise while helping others.
How to Engage
Engagement is highly encouraged through avenues like watching video tutorials on platforms such as YouTube and Bilibili. Additionally, a new website, Let's Go AI, is being developed to further facilitate access to resources.
Conclusion
Awesome-System-for-Machine-Learning stands as a rich resource for anyone interested in the systems aspect of AI and machine learning. Its comprehensive approach to compiling pivotal research, educational materials, and contributing strategies makes it an essential project for practitioners and scholars alike. The repository not only serves as a knowledge base but also as an inspiring community for collaboration and advancement in AI systems.