Fastapi For Generative Ai
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Fastapi For Generative Ai
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Author : Drake Duncan
language : en
Publisher: Independently Published
Release Date : 2025-06-08
Fastapi For Generative Ai written by Drake Duncan and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-08 with Computers categories.
FastAPI for Generative AI: Build and Deploy Scalable AI Applications with Python Unlock the power of FastAPI, Python, and Generative AI to build real-world, scalable applications that deliver blazing-fast performance and intelligent results. Whether you're integrating LLMs, diffusion models, or deploying AI APIs to production, this comprehensive guide walks you through every step with clear code, best practices, and hands-on projects. This is the definitive guide for developers, machine learning engineers, and backend architects building AI-powered web services using FastAPI. What You'll Learn Build RESTful and WebSocket-based APIs optimized for AI models Serve text-generation and image-generation models using FastAPI and Python Handle asynchronous processing, background tasks, and streaming outputs Secure endpoints with OAuth2, JWT tokens, and role-based access control (RBAC) Use Docker, GitHub Actions, and Render/Fly.io for full CI/CD deployments Integrate with Hugging Face Transformers, Diffusers, and modern AI libraries Develop a complete multi-model chat and image web app with frontend integration 1. Build Scalable AI APIs with FastAPI and Python Learn how to structure high-performance endpoints for machine learning workloads using FastAPI's async architecture. 2. Serve Generative Models Like GPT and Stable Diffusion Deploy language and image models using Hugging Face libraries, optimized for real-world inference. 3. Stream Responses with WebSockets and Server-Sent Events Deliver token-by-token LLM responses and real-time image generation feedback using FastAPI's async capabilities. 4. Secure Production-Grade AI Endpoints Implement authentication, rate limiting, and logging for mission-critical AI applications. 5. Deploy Your AI App with Docker, CI/CD, and Cloud Platforms Use containerization and GitHub Actions to launch to Render, Fly.io, or AWS. 6. Integrate Frontend Interfaces Using Streamlit or React Connect user-friendly frontends to your AI backend for real-time interaction and demo-ready delivery. 7. Real-World Project: Generative AI Chat + Image App Follow a complete walkthrough of building a multi-modal generative AI app, from architecture to deployment. Who This Book Is For Backend developers building intelligent APIs AI engineers deploying LLMs or diffusion models in production Python developers exploring modern web frameworks MLOps professionals scaling generative AI systems Teams building AI SaaS platforms, agentic tools, or custom inference endpoints Unlike generic AI or FastAPI books, FastAPI for Generative AI focuses specifically on real-time generative workloads, delivering both depth and practicality. You'll not only learn how to serve models-you'll learn how to build robust, deployable products around them.
Building Generative Ai Services With Fastapi
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Author : Alireza Parandeh
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2025-04-15
Building Generative Ai Services With Fastapi written by Alireza Parandeh and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-15 with Computers categories.
Ready to build production-grade applications with generative AI? This practical guide takes you through designing and deploying AI services using the FastAPI web framework. Learn how to integrate models that process text, images, audio, and video while seamlessly interacting with databases, filesystems, websites, and APIs. Whether you're a web developer, data scientist, or DevOps engineer, this book equips you with the tools to build scalable, real-time AI applications. Author Alireza Parandeh provides clear explanations and hands-on examples covering authentication, concurrency, caching, and retrieval-augmented generation (RAG) with vector databases. You'll also explore best practices for testing AI outputs, optimizing performance, and securing microservices. With containerized deployment using Docker, you'll be ready to launch AI-powered applications confidently in the cloud. Build generative AI services that interact with databases, filesystems, websites, and APIs Manage concurrency in AI workloads and handle long-running tasks Stream AI-generated outputs in real time via WebSocket and server-sent events Secure services with authentication, content filtering, throttling, and rate limiting Optimize AI performance with caching, batch processing, and fine-tuning techniques Visit the Book's Website.
Building Generative Ai Services With Fastapi
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Author : Ali Parandeh
language : en
Publisher: O'Reilly Media
Release Date : 2025-05-31
Building Generative Ai Services With Fastapi written by Ali Parandeh and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-31 with Computers categories.
Ready to build applications using generative AI? This practical book outlines the process necessary to design and build production grade AI services with a FastAPI web server that communicate seamlessly with databases, payment systems, and external APIs. 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. You'll learn design patterns to manage software complexity, implement FastAPI lifespan for AI model integration, handle long-running generative tasks, perform content filtering, cache outputs, implement retrieval augmented generation (RAG) with a vector database, implement usage/cost monitoring and tracking, protect services with your own authentication and authorization mechanisms, and effectively control stream outputs directly from GenAI models. You'll explore efficient testing methods for AI outputs, validation against databases, and deployment patterns using Docker for robust microservices in the cloud. Build generative services that interact with databases, external APIs, and more Learn how to load AI models into a FastAPI lifecycle memory Monitor and log model requests and responses within services Use authentication and authorization patterns hooked with generative models Handle and cache long-running inference tasks Stream model outputs via streaming events and WebSockets into browsers or files Automate the retraining process of generative models by exposing event-driven endpoints Ali Parandeh is a Chartered Engineer with the UK Engineering Council and a Microsoft and Google certified developer, data engineer, and data scientist.
Build Financial Software With Generative Ai From Scratch
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Author : Chris Kardell
language : en
Publisher: Simon and Schuster
Release Date : 2025-06-24
Build Financial Software With Generative Ai From Scratch written by Chris Kardell and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-24 with Computers categories.
Build working and regulation-compliant financial software—from scratch! The software used by banks, trading firms, and other financial services has special requirements at every level, from securing the UI to making sure backend services comply with a host of regulations. Build Financial Software with Generative AI (From Scratch) shows you how to deliver full stack financial services software—and how generative AI can make you even more productive. In Build Financial Software with Generative AI (From Scratch) you will: • Explore the core concepts of FinTech • Speed development with generative AI tools • Develop and deploy containerized services • Create and document APIs • Effectively visualize your data In Build Financial Software with Generative AI (From Scratch) you’ll build working software for processing Automated Clearing House (ACH) files, a cornerstone technology of banking that moves trillions of dollars every year. You’ll work with generative AI technology throughout the full stack application, including researching the tech for your application, spinning up a bare bone starting project, answering domain questions, clarifying functionality, and troubleshooting. Along the way, you’ll learn what sets FinTech projects apart from normal web apps. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology The financial industry is awash with regulatory and compliance challenges, complex technical requirements, and stringent security demands. There’s a huge demand for developers who can create financial services software and this book will get you started. You’ll build your own FinTech app from the ground up—with a big productivity boost from Generative AI! About the book Build Financial Software with Generative AI (From Scratch) guides you through modernizing a full-stack Automated Clearing House (ACH) application, layer-by-layer. You’ll start with a quick review of FinTech basics and an introduction to GenAI tools. Then, you’ll develop a data visualization dashboard with React, containerize components with Docker, create and refine APIs, implement backend processing, and even design a custom database. Throughout, you’ll see how AI tools aid with coding, testing, research, security, documentation, and even Agile practices. What's inside • Learn the core concepts of FinTech development • Create and document APIs using Generative AI • Build an awesome data visualization dashboard About the reader Examples are in Python. No experience with generative AI or financial services required. About the author Christopher Kardell and Mark Brouwer have both spent more than 20 years working in the Fintech industry. Table of Contents Part 1 1 Exploring FinTech and generative AI 2 Parsing payments Part 2 3 Getting started with Docker 4 APIs: The piping between our components 5 Storing our ACH files 6 Taking the next step with Next.js 7 Our minimum viable product Part 3 8 Exceptions 9 Searching and auditing 10 Company information 11 International ACH transactions and OFAC scanning 12 Where to go from here
Deep Learning For Coders With Fastai And Pytorch
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Author : Jeremy Howard
language : en
Publisher: O'Reilly Media
Release Date : 2020-06-29
Deep Learning For Coders With Fastai And Pytorch written by Jeremy Howard and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-29 with Computers categories.
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Kubernetes For Generative Ai Solutions
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Author : Ashok Srirama
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-06-06
Kubernetes For Generative Ai Solutions written by Ashok Srirama and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-06 with Computers categories.
Master the complete Generative AI project lifecycle on Kubernetes (K8s) from design and optimization to deployment using best practices, cost-effective strategies, and real-world examples. Key Features Build and deploy your first Generative AI workload on Kubernetes with confidence Learn to optimize costly resources such as GPUs using fractional allocation, Spot Instances, and automation Gain hands-on insights into observability, infrastructure automation, and scaling Generative AI workloads Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionGenerative AI (GenAI) is revolutionizing industries, from chatbots to recommendation engines to content creation, but deploying these systems at scale poses significant challenges in infrastructure, scalability, security, and cost management. This book is your practical guide to designing, optimizing, and deploying GenAI workloads with Kubernetes (K8s) the leading container orchestration platform trusted by AI pioneers. Whether you're working with large language models, transformer systems, or other GenAI applications, this book helps you confidently take projects from concept to production. You’ll get to grips with foundational concepts in machine learning and GenAI, understanding how to align projects with business goals and KPIs. From there, you'll set up Kubernetes clusters in the cloud, deploy your first workload, and build a solid infrastructure. But your learning doesn't stop at deployment. The chapters highlight essential strategies for scaling GenAI workloads in production, covering model optimization, workflow automation, scaling, GPU efficiency, observability, security, and resilience. By the end of this book, you’ll be fully equipped to confidently design and deploy scalable, secure, resilient, and cost-effective GenAI solutions on Kubernetes.What you will learn Explore GenAI deployment stack, agents, RAG, and model fine-tuning Implement HPA, VPA, and Karpenter for efficient autoscaling Optimize GPU usage with fractional allocation, MIG, and MPS setups Reduce cloud costs and monitor spending with Kubecost tools Secure GenAI workloads with RBAC, encryption, and service meshes Monitor system health and performance using Prometheus and Grafana Ensure high availability and disaster recovery for GenAI systems Automate GenAI pipelines for continuous integration and delivery Who this book is for This book is for solutions architects, product managers, engineering leads, DevOps teams, GenAI developers, and AI engineers. It's also suitable for students and academics learning about GenAI, Kubernetes, and cloud-native technologies. A basic understanding of cloud computing and AI concepts is needed, but no prior knowledge of Kubernetes is required.
Generative Ai With Langchain
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Author : Ben Auffarth
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-05-23
Generative Ai With Langchain written by Ben Auffarth and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-23 with Computers categories.
Go beyond foundational LangChain documentation with detailed coverage of LangGraph interfaces, design patterns for building AI agents, and scalable architectures used in production—ideal for Python developers building GenAI applications Key Features Bridge the gap between prototype and production with robust LangGraph agent architectures Apply enterprise-grade practices for testing, observability, and monitoring Build specialized agents for software development and data analysis Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThis second edition tackles the biggest challenge facing companies in AI today: moving from prototypes to production. Fully updated to reflect the latest developments in the LangChain ecosystem, it captures how modern AI systems are developed, deployed, and scaled in enterprise environments. This edition places a strong focus on multi-agent architectures, robust LangGraph workflows, and advanced retrieval-augmented generation (RAG) pipelines. You'll explore design patterns for building agentic systems, with practical implementations of multi-agent setups for complex tasks. The book guides you through reasoning techniques such as Tree-of -Thoughts, structured generation, and agent handoffs—complete with error handling examples. Expanded chapters on testing, evaluation, and deployment address the demands of modern LLM applications, showing you how to design secure, compliant AI systems with built-in safeguards and responsible development principles. This edition also expands RAG coverage with guidance on hybrid search, re-ranking, and fact-checking pipelines to enhance output accuracy. Whether you're extending existing workflows or architecting multi-agent systems from scratch, this book provides the technical depth and practical instruction needed to design LLM applications ready for success in production environments.What you will learn Design and implement multi-agent systems using LangGraph Implement testing strategies that identify issues before deployment Deploy observability and monitoring solutions for production environments Build agentic RAG systems with re-ranking capabilities Architect scalable, production-ready AI agents using LangGraph and MCP Work with the latest LLMs and providers like Google Gemini, Anthropic, Mistral, DeepSeek, and OpenAI's o3-mini Design secure, compliant AI systems aligned with modern ethical practices Who this book is for This book is for developers, researchers, and anyone looking to learn more about LangChain and LangGraph. With a strong emphasis on enterprise deployment patterns, it’s especially valuable for teams implementing LLM solutions at scale. While the first edition focused on individual developers, this updated edition expands its reach to support engineering teams and decision-makers working on enterprise-scale LLM strategies. A basic understanding of Python is required, and familiarity with machine learning will help you get the most out of this book.
Generative Ai Foundations In Python
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Author : Carlos Rodriguez
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-07-26
Generative Ai Foundations In Python written by Carlos Rodriguez and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-26 with Computers categories.
Begin your generative AI journey with Python as you explore large language models, understand responsible generative AI practices, and apply your knowledge to real-world applications through guided tutorials Key Features Gain expertise in prompt engineering, LLM fine-tuning, and domain adaptation Use transformers-based LLMs and diffusion models to implement AI applications Discover strategies to optimize model performance, address ethical considerations, and build trust in AI systems Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe intricacies and breadth of generative AI (GenAI) and large language models can sometimes eclipse their practical application. It is pivotal to understand the foundational concepts needed to implement generative AI. This guide explains the core concepts behind -of-the-art generative models by combining theory and hands-on application. Generative AI Foundations in Python begins by laying a foundational understanding, presenting the fundamentals of generative LLMs and their historical evolution, while also setting the stage for deeper exploration. You’ll also understand how to apply generative LLMs in real-world applications. The book cuts through the complexity and offers actionable guidance on deploying and fine-tuning pre-trained language models with Python. Later, you’ll delve into topics such as task-specific fine-tuning, domain adaptation, prompt engineering, quantitative evaluation, and responsible AI, focusing on how to effectively and responsibly use generative LLMs. By the end of this book, you’ll be well-versed in applying generative AI capabilities to real-world problems, confidently navigating its enormous potential ethically and responsibly.What you will learn Discover the fundamentals of GenAI and its foundations in NLP Dissect foundational generative architectures including GANs, transformers, and diffusion models Find out how to fine-tune LLMs for specific NLP tasks Understand transfer learning and fine-tuning to facilitate domain adaptation, including fields such as finance Explore prompt engineering, including in-context learning, templatization, and rationalization through chain-of-thought and RAG Implement responsible practices with generative LLMs to minimize bias, toxicity, and other harmful outputs Who this book is for This book is for developers, data scientists, and machine learning engineers embarking on projects driven by generative AI. A general understanding of machine learning and deep learning, as well as some proficiency with Python, is expected.
Generative Ai With Python And Pytorch Second Edition
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Author : Joseph Babcock
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-03-28
Generative Ai With Python And Pytorch Second Edition written by Joseph Babcock and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-28 with Computers categories.
Master GenAI techniques to create images and text using variational autoencoders (VAEs), generative adversarial networks (GANs), LSTMs, and large language models (LLMs) Key Features Implement real-world applications of LLMs and generative AI Fine-tune models with PEFT and LoRA to speed up training Expand your LLM toolbox with Retrieval Augmented Generation (RAG) techniques, LangChain, and LlamaIndex Purchase of the print or Kindle book includes a free eBook in PDF format Book Description Become an expert in Generative AI through immersive, hands-on projects that leverage today’s most powerful models for Natural Language Processing (NLP) and computer vision. Generative AI with Python and PyTorch is your end-to-end guide to creating advanced AI applications, made easy by Raghav Bali, a seasoned data scientist with multiple patents in AI, and Joseph Babcock, a PhD and machine learning expert. Through business-tested approaches, this book simplifies complex GenAI concepts, making learning both accessible and immediately applicable. From NLP to image generation, this second edition explores practical applications and the underlying theories that power these technologies. By integrating the latest advancements in LLMs, it prepares you to design and implement powerful AI systems that transform data into actionable intelligence. You’ll build your versatile LLM toolkit by gaining expertise in GPT-4, LangChain, RLHF, LoRA, RAG, and more. You’ll also explore deep learning techniques for image generation and apply styler transfer using GANs, before advancing to implement CLIP and diffusion models. Whether you’re generating dynamic content or developing complex AI-driven solutions, this book equips you with everything you need to harness the full transformative power of Python and AI. What will you learn Grasp the core concepts and capabilities of LLMs Craft effective prompts using chain-of-thought, ReAct, and prompt query language to guide LLMs toward your desired outputs Understand how attention and transformers have changed NLP Optimize your diffusion models by combining them with VAEs Build text generation pipelines based on LSTMs and LLMs Leverage the power of open-source LLMs, such as Llama and Mistral, for diverse applications Who this book is for This book is for data scientists, machine learning engineers, and software developers seeking practical skills in building generative AI systems. A basic understanding of math and statistics and experience with Python coding is required.
Foundations Of Generative Ai Learn Chatgpt Neural Networks Real World Ai Projects Step By Step
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Author : Everett Dean Marshall
language : en
Publisher: Gabriel Mensah
Release Date : 2025-07-07
Foundations Of Generative Ai Learn Chatgpt Neural Networks Real World Ai Projects Step By Step written by Everett Dean Marshall and has been published by Gabriel Mensah this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-07 with Computers categories.
🤖 Build Practical AI Skills—From ChatGPT to Neural Networks Looking to master AI but overwhelmed by technical jargon? Foundations of Generative AI strips away complexity and guides you through the essential skills—from ChatGPT and prompt crafting to designing neural networks and deploying projects—with clear, hands-on tutorials for beginners and enthusiasts. 🔍 What You’ll Learn Day by Day How Generative AI Works Understand what generative models are, including GANs, VAEs, and transformers, and how they produce text, images, and audio from scratch. Hands-On ChatGPT & Prompt Engineering Use real examples to write effective prompts, refine ChatGPT outputs, and integrate into real-world applications. Prompts are the bridge to AI creativity. Build and Train Neural Networks Learn the math behind backpropagation, model training, and fine-tuning—from theory to code. Step-by-Step AI Projects Launch three guided projects—including text generation and image synthesis—to turn theory into working AI applications. Inspired by Packt’s practical guides. Responsible AI & Ethics Tackle bias, hallucinations, and safety measures to build AI tools that are reliable, fair, and trustworthy. 🎯 Why This Book Stands Out Designed for First-Timers – No prior AI experience needed. Concepts are explained clearly, without overwhelming jargon. Project-Based Learning – Every chapter includes a working example you can run in minutes. Built for 2025 and Beyond – Covers the latest: GPT‑4, multimodal generators, and current AI toolkits. Insightful & Practical – Combines strong technical foundations with real-world applications and ethical best practices 💡 Benefits You’ll Gain ✅Benefit. 🔥Outcome You'll Achieve Genuine AI Literacy Understand how modern AI models generate content. Hands-On Experience Build and launch real-world AI tools using guided projects. Better AI Interactions. Master prompts to get smarter, practical outputs. Confident Deployment. Safely implement models while avoiding common pitfalls. Tech-Savvy Edge. Stand out with modern AI knowledge and capabilities. 👥 Who This Book is Perfect For Tech beginners seeking a structured and robust AI introduction Developers and professionals aiming to integrate AI into applications Students, creators, and entrepreneurs ready to build or enhance AI-powered tools Ready to start building with AI? Click Add to Cart for Foundations of Generative AI—your step-by-step guide to mastering ChatGPT, neural networks, and real-world AI applications—all without drowning in theory.