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⚡ System Design & Machine Learning Playbook

An interactive reference guide for engineers preparing for System Design, Cloud, and ML interviews.



Stars Forks PRs Welcome License: MIT Topics Interactive


🌐 Open Interactive Site  ·  📚 Browse All Topics  ·  🎯 Start Here  ·  🤝 Contribute


✨ How To Use This Repo

Most interview resources are either too scattered or too theoretical. This repo is organized around three practical tracks:

Track Best for Start here
Core System Design Distributed systems, cloud/platform, APIs, storage, scaling docs/
AI & Machine Learning ML system design, agents, classic ML, deep learning, LLMs docs/machine-learning/README.md
Reference & Practice Appendix Templates, cheat sheets, LeetCode patterns, LLD docs/reference/README.md

Use the interactive site when you want navigation, quiz mode, and progress tracking. Use the Markdown docs when you want dense references you can skim before an interview.

🚀 What Makes It Useful

Feature Description
🎯 48 interview-ready topics Core system design, cloud/platform, AI/ML, security, and interview reference material
🌙 Dark / Light mode Persisted preference, instant toggle with d
✅ Progress tracking Mark topics as read. Your progress saves locally.
🔖 Bookmarks Save topics to revisit. Accessible from any page.
🃏 Quiz / Flashcard mode Randomized flashcard review across all 48 topics
📖 Inline reader Read every topic without leaving the page — with prev/next navigation
⌨️ Keyboard-first / search, q quiz, b bookmarks, ? shortcuts
📊 Visual progress bar See your overall completion at a glance
🗺️ 3 learning paths Beginner, Mid-Level, and Advanced tracks
🔍 Live search Searches title, category, summary, and tags
🎨 Category color coding Every domain has its own visual identity
🚀 Zero setup Open in browser. No install. No build step.

🗂️ Topic Coverage (48 Topics)

🟠 Foundation (4)
🟣 Data Storage (5)
  • 🗄️ Database Selection Guide — SQL vs NoSQL tension, 7 database types with when-to-use decision matrix
  • ⚡ Caching Deep Dive — 5 cache layers, read/write patterns, eviction, cache invalidation strategies
  • 📨 Message Queues & Event Streaming — Queue vs Kafka event log, delivery guarantees, DLQ, outbox pattern
  • 🌐 Storage & CDN — Object/block/file storage, CDN pull vs push, cache invalidation
  • 🔩 Database Internals — B-tree vs LSM, indexes, replication, CDC, sharding, ACID vs BASE, isolation levels
🔵 API & Networking (4)
☁️ Cloud & Platform (5)
🟢 Distributed Systems (5)
🟡 Search & Analytics (4)
🟩 Scale & Reliability (6)
🔴 Security (4)
🩷 AI & Machine Learning (5)
  • 🤖 Machine Learning in System Design — feature store, recommendation and ranking systems, rollout strategy, drift, serving latency, rollback
  • 🧠 AI Agent System Design — planner/reactor loops, function calling, retrieval, observability, agent benchmarks, model routing, budgets, safety
  • 📈 Classic Machine Learning — Bias-variance, Naive Bayes, KNN, bagging vs boosting, SHAP/LIME, calibration, XGBoost, SVM, PCA
  • 🔬 Deep Learning — Weight init, backprop, CNNs, LSTMs, full Transformer deep-dive, GANs, VAEs, diffusion, distillation, GQA/MQA
  • 💬 LLM Interview Questions — Tokenization, RAG, LoRA/QLoRA, RLHF/DPO, scaling laws, MoE, multi-modal models, KV cache, CoT
🩵 Specialized Systems (2)
🟦 Reference (4)
  • 🎯 Common Scenarios & Solutions — 17 scenario cheat sheets covering classic patterns plus multi-tenant SaaS, webhooks, recommendation/ranking, and multi-region reliability
  • 📋 Reusable Design Templates — 12 full blueprints with architecture diagrams: YouTube, Twitter, WhatsApp, Uber, TinyURL, Rate Limiter, Metrics, TicketMaster, AI Agent, Typeahead, Google Docs, LeetCode
  • 🧩 LeetCode Question Patterns — 21 algorithm patterns with code templates: arrays, two pointers, sliding window, trees, graphs, DP, backtracking, tries, segment tree, and more
  • 🏗️ Low-Level System Design (LLD) — SOLID principles, 10 design patterns with code, 11 classic LLD questions (LRU Cache, Parking Lot, Elevator, Rate Limiter, ATM, Tic-Tac-Toe, Logger, Library)

🛤️ Learning Paths

Pick a path based on your experience level, then use the interactive site to track your progress.

🌱 Beginner — Build your foundation (6 topics)

Interview Framework → Numbers to Know → Database Selection → Caching Deep Dive → API Design & Gateway → Rate Limiting

🚀 Mid-Level — Master distributed systems and platform basics (9 topics)

Distributed Fundamentals → Cloud Fundamentals → Compute & Deployment → Resilience Patterns → Observability → High Availability → Microservices → Notifications → Authorization / MFA

🏆 Advanced — Push beyond the standard interview (8 topics)

AI Agent System Design → ML System Design → Cloud Networking → IAM / Governance → Reliability, Observability & Cost → Real-time Collaboration → Probabilistic DS → DB Internals

⌨️ Keyboard Shortcuts

Open the interactive site and press ? to see all shortcuts:

Key Action
/ Focus search
d Toggle dark mode
q Start quiz / flashcard mode
b Toggle bookmarks panel
? Show all keyboard shortcuts
Esc Close reader / clear search / close panel
Space Reveal quiz answer
→ / ← Next / previous quiz card or topic

🚀 Quick Start

Option A — Interactive site (recommended)

Open the full Interactive Website here 🌐

No install. Works offline after first load. Progress saves to your browser. Includes an inline reader, quiz/flashcard mode, dark mode, and bookmarks.

Option B — Run locally

git clone https://github.com/Ali-Meh619/System_Design_ML_Principles.git
cd System_Design_ML_Principles
# Open site/index.html in your browser — no server needed

Option C — Read on GitHub

Navigate to docs/ and click any topic. GitHub renders Markdown natively.

📁 Repository Structure

System_Design_ML_Principles/
├── site/                       # Interactive web app (no build step)
│   ├── index.html              # Main SPA — dark mode, quiz, bookmarks, inline reader
│   ├── styles.css              # Full design system with dark/light mode
│   ├── app.js                  # All interactive features
│   └── topics.js               # Topic registry with icons, difficulty, tags, paths
├── docs/                       # 48 topic documents
│   ├── foundation/             # Interview framework, estimation, I/O, networking
│   ├── api-networking/         # APIs, load balancing, rate limiting, realtime
│   ├── cloud-platform/         # Cloud foundations, deployment, networking, IAM, reliability
│   ├── data/                   # Databases, caching, queues, internals
│   ├── distributed/            # CAP, consistency, microservices, resilience, patterns
│   ├── search/                 # Full-text search, typeahead, geo, stream processing
│   ├── scale/                  # Observability, HA, ID gen, pagination, notifications
│   ├── security/               # Auth, AuthZ, privacy, secrets, threat modeling
│   ├── machine-learning/       # ML systems, agents, Classic ML, DL, LLMs
│   ├── specialized/            # Collaboration and webhook-heavy systems
│   └── reference/              # Templates, cheat sheets, LeetCode, LLD
└── assets/                     # Architecture diagram images

📖 Recommended Topic Structure

The strongest docs in this repo use a consistent interview-prep structure. Not every legacy page is identical yet, but new and upgraded docs aim to follow this pattern:

## Problem
What are we solving? When does this come up in an interview?

## Options
What are the main approaches? (with trade-off table)

## Recommended Default
What to pick and why, with the specific caveats.

## Failure Modes
What breaks? How do you detect and recover?

## Metrics
What do you measure to know it's working?

## Interview Answer Sketch
The concise 2-minute answer you'd give under time pressure.

🤝 Contributing

Contributions make this better for everyone:

  1. Fork the repo and create a branch: git checkout -b feat/your-topic
  2. Follow the recommended topic structure above — especially defaults, trade-offs, failure modes, and metrics
  3. Add the topic to site/topics.js with an icon, difficulty, and tags
  4. Open a PR using the provided template

Every substantial addition should include:

  • ✅ When to use
  • ❌ When NOT to use
  • 💥 Common failure modes
  • 📊 Measurable success metrics

See CONTRIBUTING.md for the full guide.

⭐ If this helped you

  • Star the repo — it helps others discover it
  • Share it with your team or study group
  • Open issues for topics you'd like to see covered
  • Submit PRs to improve existing content

📄 License

MIT © 2026. Free to use, share, and build on.

Built with ❤️ for engineers who take system design seriously.

⭐ Star on GitHub  ·  🌐 Open Interactive Site  ·  🐛 Report Issue