Learn to Productionize Generative AI
Learn the end-to-end steps for productionizing your own generative AI solutions using practical and project-based learning relying years of experience developing AI and data analytics solutions.
Loved by readers
- Custom-made Figures
- 160
- Practical Code Examples
- 174
- Projects
- 20
- Chapters
- 12
- Pages
- 530
- GenAI Models
- 10
Exclusive Video
See for yourself - Insights from the Book Launch event at Data Science Festival
Reviews
What Readers Think
Ali Parandeh is a true expert who is setting the trends for best practice deployment of this remarkable technology (Generative AI).
David Foster
Author of "Generative Deep Learning"
BC
Machine Learning Engineer (Amazon UK)
Excellent Resource for Transitioning from Traditional ML to Generative AI. What I enjoyed most were the hands-on, follow-along coding examples. They’re well-explained and practical—I was able to deploy my first generative AI application after just the first few chapters. It strikes a perfect balance between theory and implementation, and it focuses on real-world production concerns like scalability, performance, and deployment workflows.
QuirkyGal
Amazon UK
Exceptional book for learning how to serve Generative AI models. I had very little experience with FastAPI before starting this book, but I was eager to learn how to integrate generative AI models into applications. This book does an excellent job of introducing both the FastAPI framework and generative AI through clear explanations and practical code examples. It builds on these fundamentals to thoroughly cover the key aspects of building AI services.
L. Dalchow
Software Engineer (Amazon UK)
A well written practical guide - From Basics to Production-Grade Project. This book is exceptionally well-written and thoughtfully structured. As a Software Engineer, I also appreciated that it covered many aspects of good software design, which I don’t always see in AI books. I’ve used it both as a reference guide when I needed to brush up on specific topics, and as a comprehensive tutorial by working through it cover to cover.
Giulia
Amazon UK
It explains how to actually get generative AI projects into production without getting lost in too much theory. It covers things like handling edge cases, managing concurrency, and building real tools -- not just demos. If you’re trying to build something real with GenAI and not just prototypes, this book is worth having on your desk.
Amazon-Kunde
Amazon Germany (UK Edition)
Great explanations for production grade deployments. An engaging, enjoyable read - great for navigating the growing complexity in the GenAI tech stack. The second half of the book focuses on practical production grade considerations of how to secure, optimise, test and deploy your applications.
José Alberto Santana
Amazon Mexico
This textbook is excellent when it comes to building scalable generative AI services. Even if you have little to no knowledge of web development, you’re not going to struggle to bridge the gap between the two fields.
Yusuf
Amazon UK
Fantastic book - easy to understand, pragmatic and exceptionally useful. Brilliantly written – clear, practical, and packed with useful insights. It strikes the right balance between being beginner-friendly and offering real depth.
Sara Benchinoun
Software Engineer
A Must-Read Guide to Building AI Apps with FastAPI. It harnesses modern tech like FastAPI and RAG, offering practical, real-world knowledge for building AI applications. Simple yet powerful, it’s a go-to resource I’ll reach for to quickly develop robust AI services.
Rou
Software Developer
The Most Practical Guide to FastAPI + Generative AI Development. It doesn’t just throw code at you; it walks you through the why and how, covering everything from prompt engineering to vector databases and integrating LLMs in robust backend services.
J D
AI Developer
Fantastic book for anyone who wants to understand how to deploy Generative AI models. Not only does Ali provide a comprehensive overview of FastAPI, but he also covers other crucial topics, such as Docker and databases. Clarity, relevant examples and well-running code samples.
Aakash
Amazon India
A great book to begin your Gen AI journey. Excellent book for beginners with generative AI and looking to use fast api for building AI apps. Most of the examples work as is, which is rare for many tech books.
GS
AI Engineer
Great mix of concepts and hands on examples. I liked that it starts with concepts and transitions into coding in each chapter so you actually get to see how to build a usable AI service from scratch instead of just learning one theoretical concept after another.
Kindle Customer
Amazon UK
Great book. Covers a lot more than just FastAPI. It talks about pydantic, sqlalchemy, alembic, error handling and everything you need to deploy an AI application. Ali Parandeh has a ton of actual real-world experience on this and it shows.
Giorgio Cerruti
Director of GC Tech Consulting
I am reading the ER and it's like woow! Can't wait to read the other chapters and have my personal physical copy - I love the paper smell.
Vishnu Menon
Founder
A good friend, great colleague and excellent educator. Thank you for doing the painstaking work of keeping up with and distilling the latest AI architecture patterns. Looking forward to the full release!
Sarah D
Amazon UK
So glad I ordered this book. It’s easy to follow and a practical guide. Highly recommend it to either beginners or experienced developers.
Stephanie
Aspiring AI Developer
Great Starting Point for Aspiring AI Developers. This book provides a practical introduction to generative AI and helpful guidance for those interested in building real-world AI services. Straightforward and approachable.
anzelix
Amazon US
Good one. I’m reading this book currently. It’s really very practical. Good for someone transitioning from core DS to ai engineering.
Nahuel Alberti
Head of Engineering
Congrats Ali! I've been using a lot of FastAPI for experiments and its really great!
Aasher Kamal
Generative AI Developer
It's really a good book. I have read few chapters and learned many new things. ✨
Caspar P.
Great overview over all important parts of FastAPI with focus on compute and time heavy services. Good early access.
What's Inside
About the Book
This practical book outlines the process necessary to design and build production grade AI services with a FastAPI web server that communicate seamlessly with GenAI models, databases, authentication providers , and external APIs .
Through hands-on and visual learning with 160 custom made figures and 174 practical code examples, you’ll learn how to develop autonomous generative AI agents that stream outputs in real-time and interact with other models.
Web developers, data scientists, and DevOps engineers will learn to implement end-to-end production-ready services that leverage generative AI through practical projects.
What You'll Learn
Key Topics
- Introduction to Generative AI
-
Understanding the role of generative AI in modern applications and the rationale for using FastAPI to build these services.
- Mastering the FastAPI Web Server
-
Build production-ready web servers with FastAPI that handle authentication, validation, and error handling.
- Generative AI Integration and Serving
-
Connect to and leverage various generative AI models with streaming capabilities and proper error handling.
- Implementing Type-Safe AI Services
-
Using type annotations, dataclasses, and Pydantic models to ensure type safety in AI service development.
- Achieving Concurrency in AI Workloads
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Managing concurrent AI tasks, optimizing for I/O and compute-intensive workloads, and handling long-running inference tasks.
- Real-Time Communication
-
Implementing server-sent events (SSE) and WebSockets to stream AI-generated outputs in real-time to clients.
- AI Safety and Guardrails
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Understand GenAI attack vectors and implementing content filtering, abuse prevention, rate limiting and safety measures to ensure responsible AI service deployment.
- Prompt Engineering Fundamentals
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Master the art of crafting effective prompts for LLMs and implementing dynamic prompt templates for various use cases.
- RAG and Performance Optimization
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Implement and optimize Retrieval Augmented Generation (RAG) systems with semantic and context caching along with optimization strategies like quantization and fine-tunning.
- Database Connectivity
-
Implement robust asynchronous database connections with SQLAlchemy and vector databases for AI applications.
- Authentication & Authorization
-
Securing AI services by implementing authentication mechanisms, content filtering, throttling, and rate limiting
- Testing, Optimization, and Deployment
-
Best practices for testing AI outputs, optimizing performance through caching and batch processing, and deploying services using Docker for scalability.
Features
What makes this book different from the rest
Practical
Hands-On Learning with FastAPI
Build real-world applications with 174 practical code examples. Projects include real-time chatbots, image and audio generators, talk to documents or web, connecting databases and adding authentication.
Visual
Custom Illustrations
Learn concepts through 160 clear and engaging visuals that simplify complex ideas and make advanced topics like AI concurrency easy to understand. Also covers retrieval augmented generation (RAG), semantic caching, and more.
Broad Overview
End-to-End Coverage
Learn the the entire lifecycle of building and deploying AI services from development to real-world production deployment.
Comprehensive
Covering Diverse Topics & Technologies
Covers FastAPI, model serving, external systems integration, optimization, security, testing and deployment.
Scalable
Production-Ready
Learn techniques for creating secure and scalable AI services that perform reliably under real-world conditions.
About the Author
Hey I'm Ali Parandeh
I'm a Chartered Engineer in the UK, software engineer and data scientist with over a decade of experience designing and building scalable AI-powered products for global brands and startups.
As an AI advocate, I started London's Beginners Machine Learning meetup in 2018 to help people break into AI careers via hands-on workshops and community events. Since then, my workshops have helped 1,500+ engineers and developers master development concepts. I've also taught multiple software engineering bootcamps for Code First Girls, empowering more women to establish their tech and AI careers.
Having led engineering teams at multi-national consultancies and tech startups across various markets, I wanted to bring my experience to you in a structured book so that you avoid feeling overwhelmed and confused like I did when I was new to building generative AI tools.
If you’re into AI, FastAPI, or just curious about building apps, let's connect! 🚀
Certified with global enterprises.
Transform your career & products
Productionize Generative AI
Grab a copy of the book and level up your AI career.
- Master the Tools. Start building production-ready generative AI services using FastAPI
- Gain Hands-on Experience. Master developing GenAI services with hands-on projects
- Productionize GenAI. Learn to integrate and deploy latest GenAI models
- Future Proof Your Career. Build the AI skills that thousands of companies are seeking today
Unique Learning Experience
Learn by doing, not just reading
By following the practical projects and code examples in the book, you’ll feel more confident building your own GenAI services.
Table of Content
Your complete roadmap to generative AI productionization
Part 1: Developing AI Services
Learn to integrate a variety of generative models into a type-safe FastAPI application
1
Introduction to Generative AI
Discover why generative AI services are the cornerstone of future applications. Learn how they enhance creativity, personalize user experiences, and automate complex tasks, all while addressing barriers to adoption. This chapter sets the stage with an overview of the capstone project.
2
Getting Started with FastAPI
Discover FastAPI, the modern framework for building scalable APIs. Understand its features, limitations, and how it compares to other web frameworks. Start creating FastAPI applications, progressively organize projects, and migrate from frameworks like Flask or Django.
3
AI Integration and Model Serving
Learn how to serve generative AI models, including language, audio, vision, and 3D models. Explore strategies for efficient model serving, such as preloading, externalizing, and monitoring models with middleware.
4
Implementing Type-Safe AI Services
Master type safety with Pydantic and Python’s type annotations. Implement validated, secure models and environments using compound models, custom validators, and serialization techniques.
Part 2: Communicating with External Systems
Learn to build AI services integrated with external systems for concurrent users that are capable of streaming GenAI outputs.
5
Achieving Concurrency in AI Workloads
Optimize generative AI services for multiple users with asynchronous programming. Manage I/O tasks, event loops, and long-running processes. Includes projects like a web scraper and retrieval-augmented generation.
6
Real-Time Communication with Generative Models
Compare communication mechanisms like polling, SSE, and WebSockets. Build real-time endpoints for streaming AI outputs and design APIs for dynamic data flows, including LLM interactions.
7
Integrating Databases into AI Services
Explore relational and NoSQL databases for storing and managing user interactions with generative AI. Build CRUD endpoints and manage schema changes. Learn to store data from real-time streams.
bonus: available
Bonus: Introduction to Databases for AI
Determine when a database is necessary and identify the appropriate database type for your project. Understand the underlying mechanism of relational databases and the use cases of non-relational databases in AI workloads.
Download NowPart 3: Security, Optimization, Testing and Deployment
Learn to build additional layers of security, optimization and testing into your AI services then how to deploy them
8
Authentication & Authorization
Implement robust authentication and authorization methods, including JWT and OAuth. Dive into access control models like RBAC, ABAC, and hybrid approaches for secure AI services.
9
Securing AI Services
Protect your AI services with usage moderation, input/output guardrails, and rate-limiting techniques.
10
Optimizing AI Services
Optimize performance using caching, model quantization, and prompt engineering for better scalability and efficiency.
11
Testing AI Services
Tackle the challenges of testing generative AI, from flakiness and resource constraints to adversarial attacks. Learn testing strategies with unit, integration, and E2E tests through practical projects like RAG systems.
12
Deployment & Containerization of AI Services
Deploy generative AI services using virtual machines, containers, and serverless platforms. Learn containerization with Docker, GPU integration, and optimization techniques for lightweight deployments.
bonus
Scaling AI Services
Learn to scale AI service using managed app service platforms in the cloud such as Azure App Service, Google Cloud Run, AWS Elastic Container Service and self-hosted Kubernetes orchestration clusters.
Transform your career & products
Productionize Generative AI
Grab a copy of the book and level up your AI career.
- Master the Tools. Start building production-ready generative AI services using FastAPI
- Gain Hands-on Experience. Master developing GenAI services with hands-on projects
- Productionize GenAI. Learn to integrate and deploy latest GenAI models
- Future Proof Your Career. Build the AI skills that thousands of companies are seeking today
FAQ