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 early release readers
Reviews
He is a true expert who is setting the trends for best practice deployment of this remarkable technology.
David Foster
Partner at Applied Data Science Partners
Author of "Generative Deep Learning"
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.
Aasher Kamal
Generative AI Developer
It's really a good book. I have read few chapters and learned many new things. ✨
Nahuel Alberti
Head of Engineering
Congrats Ali! I've been using a lot of FastAPI for experiments and its really great!
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!
Caspar P.
Great overview over all important parts of FastAPI with focus on compute and time heavy services. Good early access.
What's Inside
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
Understanding the role of generative AI in modern applications and the rationale for using FastAPI to build these services.
Build production-ready web servers with FastAPI that handle authentication, validation, and error handling.
Connect to and leverage various generative AI models with streaming capabilities and proper error handling.
Using type annotations, dataclasses, and Pydantic models to ensure type safety in AI service development.
Managing concurrent AI tasks, optimizing for I/O and compute-intensive workloads, and handling long-running inference tasks.
Implementing server-sent events (SSE) and WebSockets to stream AI-generated outputs in real-time to clients.
Understand GenAI attack vectors and implementing content filtering, abuse prevention, rate limiting and safety measures to ensure responsible AI service deployment.
Master the art of crafting effective prompts for LLMs and implementing dynamic prompt templates for various use cases.
Implement and optimize Retrieval Augmented Generation (RAG) systems with semantic and context caching along with optimization strategies like quantization and fine-tunning.
Implement robust asynchronous database connections with SQLAlchemy and vector databases for AI applications.
Securing AI services by implementing authentication mechanisms, content filtering, throttling, and rate limiting
Best practices for testing AI outputs, optimizing performance through caching and batch processing, and deploying services using Docker for scalability.
Features
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.
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.
Learn the the entire lifecycle of building and deploying AI services from development to real-world production deployment.
Covers FastAPI, model serving, external systems integration, optimization, security, testing and deployment.
Learn techniques for creating secure and scalable AI services that perform reliably under real-world conditions.
About the Author
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.
Grab a copy of the book and level up your AI career.
Unique Learning Experience
By following the practical projects and code examples in the book, you’ll feel more confident building your own GenAI services.
Table of Content
Learn to integrate a variety of generative models into a type-safe FastAPI application
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.
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.
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.
Master type safety with Pydantic and Python’s type annotations. Implement validated, secure models and environments using compound models, custom validators, and serialization techniques.
Learn to build AI services integrated with external systems for concurrent users that are capable of streaming GenAI outputs.
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.
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.
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.
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.
Learn to build additional layers of security, optimization and testing into your AI services then how to deploy them
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.
Protect your AI services with usage moderation, input/output guardrails, and rate-limiting techniques.
Optimize performance using caching, model quantization, and prompt engineering for better scalability and efficiency.
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.
Deploy generative AI services using virtual machines, containers, and serverless platforms. Learn containerization with Docker, GPU integration, and optimization techniques for lightweight deployments.
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.
Grab a copy of the book and level up your AI career.
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