🧭 AI/ML and System Architecture Article Map — From Theory to Practice

📚 Table of Contents¶

1. Introduction: Why You Need This Map
2. Station 1: Fundamentals and Core Questions
3. Station 2: Deep Dives into Models and Generative AI
4. Station 3: From Lab to Production (MLOps & System Architecture)
5. Station 4: Trends, Tools, and Real-World Cases
6. Closing: How to Use This Map

🌟 Introduction: Why You Need This Map¶

The scope of the field (Hook):
AI, ML, and system architecture are among the hottest and most complex domains in tech today — knowledge is dense and the pace of change is relentless.

The problem and the solution:
Insights are scattered across dozens of articles, making it hard to learn in a coherent sequence. This guide is your personal content map, helping you find the best starting point and reading path based on your current level and goals.

Who this is for:
- 🧩 Beginners: wanting to build a clear, structured knowledge base
- 🧠 Intermediate developers: wanting to go deep on specific algorithms or frameworks
- 🏗️ Senior architects: looking for production deployment experience to reference

🗺️ Station 1: Fundamentals and Core Questions (The Fundamentals)¶

Audience: Beginners, product managers, and non-technical readers who want to understand core concepts.
Core goal: Build a sense of the layers between AI, ML, and deep learning, and grasp common issues in the training process.

Introduction to AI and Machine Learning¶

1. Hung-yi Lee ML 2021: Basic Concepts of Machine Learning
2. Guide to Supervised and Unsupervised Learning (TBD)
3. Hung-yi Lee ML 2021: Training Techniques and Regularization
4. GenAI & ML Intro 2025 — Why Context Engineering Is the Key Technology in the AI Agent Era
5. GenAI & ML Intro 2025 — Deep Anatomy of Large Language Model Internals
6. GenAI & ML Intro 2025 — Technical Challenges and Pitfalls in Capability Benchmarking
7. GenAI & ML Intro 2025 — Understanding Machine Learning and Deep Learning

Model Evaluation and Tuning¶

1. Hung-yi Lee: Complete Guide to Model Evaluation Metrics (TBD)
2. Hung-yi Lee II: Solutions for Overfitting and Underfitting (TBD)
3. Hung-yi Lee: Data Preprocessing and Feature Engineering in Practice (TBD)
4. Hung-yi Lee ML 2021: Inside the Black Box — Explainable Machine Learning (XAI) Overview

🛠️ Station 2: Deep Dives into Models and Generative AI (Deep Dives & GenAI)¶

Audience: Developers with foundational knowledge who want to build and understand the principles.
Core goal: Go deep into mainstream model architectures and generative AI applications.

Mainstream Deep Learning and LLM Architectures¶

1. Hung-yi Lee ML 2021: Convolutional Neural Networks (CNN)
2. Hung-yi Lee: RNN and LSTM in Sequential Data Applications (TBD)
3. Hung-yi Lee ML 2021: Transformer and the Core LLM Architecture
4. Why Does Transformer Need Positional Encoding?

Advanced and Specialized ML Applications¶

1. Hung-yi Lee ML 2021: Self-Attention Mechanism Principles
2. Hung-yi Lee ML 2021: GAN — Generative Adversarial Networks
3. Hung-yi Lee ML 2021: Autoencoder
4. Hung-yi Lee ML 2021: Self-Supervised Learning — BERT
5. Hung-yi Lee ML 2021: Handling Domain Shift — Domain Adaptation and Generalization
6. Hung-yi Lee ML 2021: Anomaly Detection
7. Hung-yi Lee ML 2021: Adversarial Attacks and Defense
8. Hung-yi Lee ML 2021: Reinforcement Learning — Core Concepts and Operational Framework
9. Hung-yi Lee ML 2021: Network Compression
10. Hung-yi Lee ML 2021: Lifelong Learning — How Machines Learn to Keep Learning
11. Hung-yi Lee ML 2021: Meta-Learning — Learning How to Learn
12. Hung-yi Lee ML 2023: Introduction to Diffusion Models

Large Language Model (LLM) Core Mechanisms and Applications¶

1. GenAI Era ML — LLM Training Tools in the Generative AI Age
2. GenAI Era ML — Technical Breakthroughs and Future Directions
3. GenAI Era ML — Dissecting the Internal Mechanics of Language Models
4. GenAI Era ML — Transformer's Competitors
5. GenAI Era ML — The Power and Limits of Pretrain-Alignment
6. GenAI Era ML — Post-Training, Catastrophic Forgetting, and Preserving Model Capability
7. GenAI Era ML — Large Language Model Reasoning Ability
8. GenAI Era ML — Challenges in LLM Evaluation and Efficient Inference
9. GenAI Era ML — Model Merging and Model Editing
10. GenAI Era ML — Beyond Text: Multimodal Dialogue and Generation
11. RAG Deep Dive: From Principles to Architectural Practice
12. RAG vs. Fine-Tuning: A Comparison
13. Fine-Tuning In Depth
14. LLaMA 3.2: Deployment, Testing, and Prompting
15. Gemini API: Getting Started and Applications
16. Hung-yi Lee 2026 Course — Accelerating Language Model Generation Speed
17. Hung-yi Lee 2026 Course — Harness Engineering

🏗️ Station 3: From Lab to Production (MLOps & System Architecture)¶

Audience: Senior developers and system architects.
Core goal: End-to-end practice from model deployment and monitoring to full system design.

MLOps Workflow and CI/CD¶

1. Introduction to MLOps and Workflow Design (TBD)
2. Deploying Model APIs with Docker and Kubernetes (TBD)
3. Getting Started with GitHub Actions Automation
4. From Command Line to Cloud: Deploying a Modern Python Service with gcloud CLI
5. LINE Bot in Practice: From Webhook to Cloud Run
6. LINE Bot Scheduled Push Notifications with Google Cloud Scheduler

Cloud-Native and Containerized Architecture¶

1. Kubernetes Series I: Containerization Basics
2. Kubernetes Series II: Kubernetes Fundamentals
3. Kubernetes Series III: Advanced Topics and Deployment Strategies
4. Kubernetes Series IV: Enterprise Platform OpenShift
5. Microservice Architecture and ML System Integration (TBD)
6. Data Pipeline Design: From ETL to ELT (TBD)

🔭 Station 4: Trends, Tools, and Real-World Cases (Future, Tools & Cases)¶

Audience: Readers who want to follow the latest trends and see applied examples.
Core goal: Combine industry case studies with hands-on tools to spark ideas and applied thinking.

Trends, Tools, and Applied Cases¶

1. LINE Bot Anti-Scam with Google Cloud — A Practical Project Walkthrough
2. Smart Traffic: Building a Violation Detection System with Computer Vision
3. AI Tools: A Guide to Rapid Development and Product Iteration
4. Website Automation: Hugo + GitHub Actions Static Site in Practice
5. Google AI Agent Development Kit (ADK) — Intensive Course Introduction
6. Prompting: Unlock Your AI — Introduction to the Model Context Protocol (MCP)
7. Python Toolbox: Automating Slide Decks with python-pptx
8. From Vibe Coding to Practice: How I Rebuilt My Personal Website with Google AI Tools
9. Vibe Coding Retrospective: From Writing Code to Navigating Code
10. Getting Started with Hardware AI Using ESP32
11. OpenClaw: Introduction and Positioning
12. Building the AI Collaboration Future: MCP, A2A, and ACP — Roles and Applications
13. Image Generation vs. Stateful Tool Agent — A Comparison
14. Official vs. Community: Claude Code vs. Roo Code
15. Frustrated That a Masterpiece Got a Bad Ending? Exploring AI as "Storyboard Editor" to Restart Manga Endings
16. From Prompt to Harness: When AI Giants Start Harvesting Startups — Where Is the Engineer's Moat?
17. Refusing the Wild Horse: A Developer's Guide to Context Engineering for Restarting Manga Endings with AI
18. When Coding Becomes Code Review: A Senior Developer's AI Collaboration Survival Guide

✅ Closing: How to Use This Map¶

• Beginners: Start at Station 1 — Fundamentals — to build a solid foundation.
• Architects: Jump directly to Station 3 — Production — and focus on the MLOps and system design articles.