Structure and Content The book is organized into four comprehensive parts, each addressing critical aspects of backend development and AI integration, ensuring a balance between foundational knowledge and practical application: Part I: Foundations of Modern Backend Engineering introduces FastAPI, a Python framework known for its speed and type safety through Pydantic. It guides readers through setting up development environments with Python 3.12, virtual environments, and PostgreSQL. Beginners learn to build their first FastAPI application, utilizing features like dependency injection, asynchronous programming, and automatic OpenAPI documentation for streamlined development. - Part II: Building Secure and Scalable APIs delves into advanced topics such as authentication (JWT, OAuth2, role-based access control), input validation, and API versioning. It emphasizes security practices, including rate limiting, CORS configuration, and secrets management, to safeguard APIs. Readers explore middleware, background tasks, and file uploads to enhance API functionality and performance. - Part III: AI as a Service focuses on integrating AI into APIs, covering OpenAI’s GPT models, text embeddings, and machine learning pipelines. Practical examples include building sentiment analysis APIs, semantic search systems, and content generation services using libraries like scikit-learn, FAISS, and sentence-transformers. - Part IV: Real-World Projects and Use Cases presents end-to-end projects, such as a user management system with CRUD operations, a resume parsing and job-matching API, and an AI-powered support ticket system. These projects integrate FastAPI, SQLAlchemy, Docker, and AI models, demonstrating production-grade workflows. The book concludes with valuable appendices, including a FastAPI/Pydantic syntax reference, OpenAI prompt templates, Docker deployment configurations, Postman collections for API testing, and a glossary of AI and backend terms. It also offers interview preparation tips and system design questions to aid career advancement. Key Features Practical Projects : Step-by-step tutorials guide readers through building applications like a sentiment analysis API, a semantic search engine, and a support ticket system, complete with code, testing, and deployment instructions. - AI Integration : The book teaches how to incorporate OpenAI’s GPT and embedding APIs, as well as Hugging Face models, to create intelligent services such as content generation and automated ticket classification. - Scalability Techniques : Readers learn to optimize APIs with horizontal scaling (using Gunicorn and Uvicorn), caching (Redis), rate limiting, and load balancing for high-demand environments. - DevOps and Deployment : Detailed instructions cover Docker containerization, CI/CD pipelines with GitHub Actions, and deployment to platforms like Render, Railway, and Fly.io, ensuring seamless production workflows. This book is tailored for: Intermediate to Advanced Python Developers seeking to build production-grade APIs with FastAPI. - Data Scientists and Machine Learning Engineers looking to deploy AI models as scalable services. - DevOps Professionals interested in modern deployment pipelines using Docker and CI/CD.