This repository is a curated reading list for researchers and engineers working at the intersection of machine learning (ML) and systems (SysML). It provides a comprehensive collection of seminal papers, frameworks, and research directions, aiming to bridge the gap between ML algorithms and efficient system implementations.

How It Works

The list is organized thematically, covering key areas such as distributed ML frameworks, runtime systems, serving infrastructure, scheduling, algorithmic aspects, testing, interpretability, model management, hardware acceleration, security, and applied ML platforms. It serves as a structured guide to the evolving landscape of SysML research.

Quick Start & Requirements

This is a static reading list; no installation or execution is required. The content consists of links to research papers and related resources.

Highlighted Details

• Covers foundational papers on deep learning and distributed ML.
• Includes key systems for large-scale ML like TensorFlow, Ray, and MXNet.
• Features research on hyperparameter optimization, model search, and automated ML.
• Addresses critical aspects of ML serving, inference, and runtime execution.

Maintenance & Community

The list is maintained by Marco Canini and welcomes contributions via pull requests.

Licensing & Compatibility

The repository itself is likely under a permissive license (e.g., MIT, Apache 2.0) given its nature as a curated list. The linked papers are subject to their respective publisher's licenses and copyright.

Limitations & Caveats

As a reading list, it does not provide code or implementations. The content reflects research up to the last update, and newer advancements may not be included.