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Part II: Step-by-Step AI/ML Project Development

“You don’t need a PhD to build an AI app—just a clear path from folder to frontend.”

Part II is where we roll up our sleeves and build real AI/ML projects from the ground up. This section walks you through every critical step—from organizing your repository to writing inference code, building a frontend, and integrating with paid APIs.

Unlike generic tutorials, this part focuses on modularity, deployment readiness, and cost-efficiency—giving you repeatable patterns you can apply to any AI idea.

✅ Chapter 3: Setting Up Your Project Repository

  • Learn how to structure your project folders for clean separation between backend and frontend. We’ll create a Python virtual environment, set up requirements.txt, README.md, and connect your GitHub repo with version control best practices.

✅ Chapter 4: Building the ML Logic

  • This chapter covers both options:
    (A) Running a local pretrained model like CartoonGAN using PyTorch, or
    (B) Using a cloud API (OpenAI, Replicate) for inference.

  • You’ll learn how to write clean inference code, test your outputs, and prepare for integration.

✅ Chapter 5: Building the Frontend UI

  • Choose your frontend framework—Vite or Create React App (CRA)—and build an engaging UI that interacts with your AI backend. We’ll cover styling tips (dark mode, export buttons), managing input/output states, and fetching predictions in real-time.

✅ Chapter 6: Integrating with Paid APIs

  • Whether you’re using GPT for chatbot responses or Stability AI for image generation, this chapter shows how to integrate safely and affordably. We’ll also explore .env files, secret management, fallback logic, and how to avoid surprise bills.

After Part II, You Will Be Able To:

  • Set up a clean, scalable AI/ML project repo with modular structure
  • Write and test ML inference code using local or cloud models
  • Build frontend interfaces that call your AI backend or API
  • Securely manage API keys and environment secrets
  • Launch fully working prototypes with minimal cost

This part is the heart of the book—where code meets creativity, and ideas turn into apps.