This repository serves as a comprehensive reference for system design interview questions, focusing on machine learning applications across various domains. It provides curated links to academic papers, blog posts, and technical articles covering topics from data warehousing and ensemble learning to visual search, content moderation, and recommendation systems. The target audience includes software engineers, ML practitioners, and students preparing for system design interviews, offering a structured approach to understanding complex ML system architectures.

How It Works

The repository is organized by chapters, each dedicated to a specific system design problem. Within each chapter, a detailed list of references is provided, covering foundational concepts, specific algorithms, and real-world implementations. This approach allows users to dive deep into each topic, exploring various techniques and trade-offs involved in building scalable and efficient ML systems.

Quick Start & Requirements

This repository is a collection of links and does not require installation or execution. Users can access the information directly through their web browser.

Highlighted Details

• Extensive coverage of core ML concepts: data preprocessing, model training, evaluation metrics, and deployment strategies.
• Detailed exploration of various ML architectures: from traditional algorithms to deep learning models like Transformers and CNNs.
• Real-world case studies: including systems from major tech companies like Google, Facebook, and LinkedIn.
• Focus on practical challenges: addressing issues like cold start, data leakage, bias, and scalability.

Maintenance & Community

No specific information on contributors, community channels, or roadmap is provided in the README.

Licensing & Compatibility

The repository itself is a collection of links and does not have a specific license. The linked resources may have their own licenses.

Limitations & Caveats

This repository is a curated list of references and does not contain any executable code or implementations. Users will need to consult the linked resources for practical application and further details.