Quick reference

AI Full-Stack

Ship real features on top of models — RAG, agents, evaluation and inference.

Difficulty: advancedTime: 25+ hours10 of 97 sections written

AI full-stack engineering is building real, deployed products on top of machine learning and language models — the full chain from data and a trained model through evaluation, a serving pipeline, and the frontend a user actually touches, not just the model itself.

Sections

97 sections
  1. AI and Machine Learning Fundamentals — Must Be Strongbeginner55 min
  2. Mathematics for AI — Practical Levelbeginner50 min
  3. Python for AIbeginner45 min
  4. Data Fundamentalsintermediate60 min
  5. Feature Engineeringintermediate55 min
  6. Classical Machine Learningintermediate55 min
  7. Model Evaluationintermediate65 min
  8. ML Pipelinesintermediate50 min
  9. Deep Learning Fundamentalsintermediate65 min
  10. PyTorchintermediate55 min
  11. Computer Vision7 items
  12. Natural Language Processing5 items
  13. Transformers — Core Knowledge9 items
  14. LLM Fundamentals8 items
  15. LLM APIs and SDKs8 items
  16. Prompt Engineering8 items
  17. LLM Context Engineering6 items
  18. Tokenization and Token Economics6 items
  19. Embeddings6 items
  20. Vector Databases and Search7 items
  21. RAG — Retrieval-Augmented Generation10 items
  22. Advanced RAG10 items
  23. RAG Evaluation6 items
  24. AI Agents9 items
  25. Agent Design Patterns9 items
  26. Tool Calling9 items
  27. Agent Memory8 items
  28. Multimodal AI8 items
  29. Document AI8 items
  30. Generative Media7 items
  31. Fine-Tuning and Adaptation9 items
  32. Model Quantization and Compression5 items
  33. Local and Self-Hosted Models6 items
  34. Inference Engineering10 items
  35. AI Model Selection4 items
  36. AI Evaluation — Critical Production Skill9 items
  37. Experiment Tracking5 items
  38. AI Observability and Tracing4 items
  39. AI Cost Engineering9 items
  40. AI Reliability8 items
  41. AI Security Fundamentals11 items
  42. Secure AI Agent Architecture10 items
  43. AI Privacy and Governance10 items
  44. AI Safety and Responsible Development9 items
  45. AI Red Teaming10 items
  46. Structured Outputs8 items
  47. AI + Backend Architecture5 items
  48. AI + Full-Stack Frontend10 items
  49. AI UX Design7 items
  50. AI APIs and Streaming Protocols7 items
  51. AI Databases and Storage6 items
  52. AI Data Pipelines7 items
  53. AI Search Systems8 items
  54. Recommendation Systems — Fundamentals8 items
  55. AI Personalization7 items
  56. Speech AI8 items
  57. AI Computer Use and Automation7 items
  58. AI Code Generation and Developer Tools7 items
  59. AI Agents for Software Engineering10 items
  60. MLOps Fundamentals10 items
  61. LLMOps / GenAI Operations10 items
  62. Model and Dataset Versioning3 items
  63. Model Monitoring11 items
  64. Data and Model Drift8 items
  65. Deployment Strategies for AI9 items
  66. GPU Fundamentals for Developers9 items
  67. AI Infrastructure11 items
  68. AI System Design4 items
  69. AI System Design Example — RAG Application1 item
  70. AI System Design Example — Agent Platform1 item
  71. AI System Design Example — Voice Agent1 item
  72. AI System Design Example — Document Intelligence1 item
  73. AI + Payments / High-Impact Workflows3 items
  74. AI Testing Strategy7 items
  75. AI Evaluation-Driven Development5 items
  76. AI Reliability Patterns2 items
  77. AI Prompt Injection Defense6 items
  78. AI Supply Chain Security8 items
  79. AI Governance and Compliance10 items
  80. AI Product Analytics3 items
  81. AI A/B Testing6 items
  82. AI Quality Rubrics3 items
  83. AI Human-in-the-Loop5 items
  84. AI Business and Product Thinking7 items
  85. AI Full-Stack Security Architecture1 item
  86. AI Full-Stack Performance8 items
  87. AI API Rate Limiting and Quotas8 items
  88. AI Data Caching3 items
  89. AI Model Routing4 items
  90. AI Architecture Anti-Patterns10 items
  91. AI Interview Knowledge — Core Questions14 items
  92. AI Interview Knowledge — System Design Questions8 items
  93. Recommended AI Tools and Ecosystem6 items
  94. Priority for a Full-Stack Developer3 items
  95. Practical Projects — Build in This Order7 items
  96. Suggested Learning Order1 item
  97. Target Outcome1 item

Why it matters

  • Most AI value today ships as a feature inside an ordinary product, built by engineers who are not ML researchers but need to reason about models, data and evaluation correctly
  • A model that looks accurate in a notebook and a model that survives production are different achievements — leakage, training/serving skew and distribution shift all hide until deployment
  • Evaluation is where most real mistakes happen: accuracy and ROC-AUC both mislead under class imbalance, and a model metric can improve while the business outcome gets worse
  • The vocabulary here — bias/variance, precision/recall, gradients, tensors — is what makes a design or interview conversation about an ML feature precise instead of hand-wavy

Where it is used

  • Building or reviewing a feature that calls a trained model or an LLM in production
  • Diagnosing why a model that scored well offline performs worse after deployment
  • ML/AI system design interviews for full-stack and backend roles
  • Choosing and evaluating a model for a business problem, not just training one

The big picture

The path from raw data to a served prediction
preparedintofitsscoredhonestlydeployed

Data

collected, cleaned, split

Features

engineered, encoded, scaled

Training

forward pass, loss, backprop

Evaluation

metrics that match the real goal

Serving

online or batch inference, monitored

  • Data — collected, cleaned, split
    • leads to Features (prepared into)
  • Features — engineered, encoded, scaled
    • leads to Training (fits)
  • Training — forward pass, loss, backprop
    • leads to Evaluation (scored honestly)
  • Evaluation — metrics that match the real goal
    • leads to Serving (deployed)
  • Serving — online or batch inference, monitored