Deploying Machine Learning Models With Fastapi And Onnx
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Deploying Machine Learning Models With Fastapi And Onnx
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Author : Maurice H Connor
language : en
Publisher: Independently Published
Release Date : 2025-12-16
Deploying Machine Learning Models With Fastapi And Onnx written by Maurice H Connor and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-16 with Computers categories.
Deploying Machine Learning Models with FastAPI and ONNX: A Practical Guide to Scalable AI Applications Are you ready to bring your machine learning models to life? If the idea of deploying AI feels daunting, you're not alone. Many beginners find the deployment phase of machine learning to be one of the most intimidating challenges. But don't worry this book will guide you, step by step, through the process in a way that's both approachable and empowering. Whether you're a developer eager to level up your skills or a beginner with no prior technical experience, Deploying Machine Learning Models with FastAPI and ONNX is the perfect companion for your journey into scalable AI applications. This practical, hands-on guide is designed to take you from the basics to production-ready deployment, even if you're starting from scratch. What's Inside This Book? No Technical Jargon, Just Practical Steps: You don't need a background in AI or complex coding languages to get started. Every concept is explained in simple, easy-to-follow steps that build your confidence and skills as you go. Real-World Applications: You'll learn how to deploy machine learning models into production with FastAPI and ONNX. By the end of this book, you'll be equipped to serve real-time predictions in a scalable, reliable way-skills you can apply immediately to real-world projects. Step-by-Step Guidance: This book is structured to take you through each stage of the deployment pipeline-from preparing and training your model to integrating it into a fast, efficient API. No more overwhelming theory-only practical, actionable advice. Celebrate Small Wins: Mistakes are a part of the learning process, and in this book, we embrace them! You'll see how to troubleshoot common challenges and celebrate your progress as you deploy your first models. Comprehensive, Yet Accessible: Designed for both beginners and developers looking to expand their knowledge, this guide breaks down every step and provides you with the tools and support needed to succeed. Key Benefits You'll Gain: Master the fundamentals of deploying AI models using FastAPI and ONNX. Build production-ready APIs for real-time model serving and scalable AI applications. Learn how to handle real-world challenges like model performance, optimization, and inference speed. Get comfortable with model versioning, error handling, and continuous integration. Gain practical experience with deployment on cloud platforms and edge devices. Learn to debug, test, and scale your AI applications with confidence. Why This Book is Different: Beginner-Friendly: No need to be an expert in machine learning or coding to follow along. The friendly tone and approachable style make complex concepts easier to grasp. Hands-On Learning: Focused on practical, real-world applications, this book will teach you skills that are immediately useful and in-demand in the tech industry. Scalable Solutions: You'll learn to deploy models not just for testing, but in real production environments where they can scale to meet user needs. Start Your Journey Today Whether you're exploring AI for the first time or seeking a structured way to level up your deployment skills, this book is your ultimate guide to fast, efficient, and scalable machine learning deployments. Ready to transform your knowledge into real-world applications? Grab your copy today, and let's get your machine learning models deployed and serving real-time predictions in no time!
Machine Learning Deployment Made Simple
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Author : Bryan C Diego
language : en
Publisher: Independently Published
Release Date : 2025-12
Machine Learning Deployment Made Simple written by Bryan C Diego and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12 with Computers categories.
Machine Learning Deployment Made Simple: FastAPI, ONNX, and Python for Modern AI Systems Are you curious about artificial intelligence but feel overwhelmed by jargon, code, and complicated tools? Do you want to see your own machine learning models come to life-deployed and working in real-world applications-even if you've never written a line of Python before? This book is your friendly invitation into the world of modern AI deployment, designed especially for complete beginners and those who think "I'm not technical enough." Take the Fear Out of AI and Coding You don't need a PhD or a programming background to master machine learning deployment. With warmth, encouragement, and clear explanations, this book guides you step by step, from your very first install to running your own machine learning models online. You'll learn with real examples and build practical skills at your own pace. Every chapter is crafted to empower, reassure, and nurture your progress, no matter how much (or how little) experience you start with. What Makes This Book Different? Beginner-Friendly and Non-Intimidating Every concept-whether it's FastAPI, ONNX, or Python basics-is broken down into small, digestible steps, with plain English explanations and hands-on examples that anyone can follow. Mistakes Are Welcome The book normalizes the learning curve. You'll see how errors, bugs, and detours are not failures-they're stepping stones. Each chapter celebrates your small wins and encourages you to keep going, making learning to code and deploy AI a joyful, pressure-free experience. Real-World, Practical Skills Move beyond theory. You'll create and deploy actual machine learning models, connect them to web APIs, and see how they can solve real problems-from image recognition to simple data predictions. By the end, you'll have projects you can share and be proud of. Step-by-Step Confidence Building You'll set up your development environment, understand essential Python and machine learning foundations, and use FastAPI and ONNX to turn your models into deployable applications. Each chapter builds your skills naturally, giving you a sense of accomplishment at every stage. Supportive and Encouraging Tone Written as a supportive companion, the book reassures you: "You can do this!" Practical checkpoints, troubleshooting tips, and gentle explanations are there whenever you feel stuck. Key Takeaways and Benefits: Set up Python and all necessary tools, even if you're a total beginner. Train, convert, and deploy machine learning models using industry-standard frameworks (FastAPI, ONNX, Python). Learn best practices for API development, security, and performance-demystified for beginners. Gain confidence with coding and problem-solving by seeing mistakes as part of the learning journey. Discover how AI can solve real-world problems and how you can be part of this exciting field, no matter your background. Your Invitation to a New Skillset If you've ever doubted your ability to enter the tech world, this book is written for you. With "Machine Learning Deployment Made Simple," you'll unlock the tools, guidance, and motivation you need to start coding, deploying, and creating with AI-no experience required. Don't let uncertainty hold you back. Pick up this book and let's take the first step together toward a future where you turn ideas into reality, one line of code at a time. Start your empowering AI journey today-your future as a confident creator begins here.
Building And Training A Gpt Model A Comprehensive Code Tutorial
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Author : Othman Omran Khalifa
language : en
Publisher: Othman Omran Khalifa
Release Date : 2025-12-23
Building And Training A Gpt Model A Comprehensive Code Tutorial written by Othman Omran Khalifa and has been published by Othman Omran Khalifa this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-23 with Computers categories.
The rapid advancement of artificial intelligence particularly in the field of large language models has fundamentally transformed how machines understand and generate human language. Among these models, Generative Pre-trained Transformers (GPT) have emerged as one of the most influential architectures, driving breakthroughs in natural language processing, software development, scientific research, education, and countless real-world applications. Despite their widespread use, the internal mechanisms and training processes of GPT models often remain opaque to many learners and practitioners. This book, Building and Training a GPT Model: A Comprehensive Code Tutorial, was written to bridge that gap. Rather than treating GPT models as black-box tools, this work invites readers to explore their inner workings in a structured, practical, and accessible manner. The primary goal is to empower readers to move beyond model usage toward true model understanding designing, implementing, training, evaluating, and deploying GPT-style architectures from the ground up. The book is intentionally hands-on and code-driven. Each concept is introduced with clear theoretical explanations and immediately reinforced through practical implementations using Python, PyTorch, and Hugging Face Transformers. From tokenization and attention mechanisms to pre-training objectives, optimization strategies, and deployment pipelines, readers are guided step by step through the full lifecycle of a GPT model. Wherever possible, examples are drawn from real-world scenarios to highlight both academic relevance and practical impact. This book is intended for a broad audience, including senior undergraduate and postgraduate students, researchers, engineers, and professionals who seek a deeper, research-level understanding of generative AI. While some familiarity with machine learning and Python programming is assumed, the material is presented in a progressive manner that allows motivated readers to build confidence as they advance through the chapters. Each chapter has been carefully designed to stand on its own while contributing to a coherent end-to-end learning journey. Early chapters focus on architectural foundations and data preparation, followed by detailed discussions on model construction and training. Later chapters address evaluation, optimization, deployment, and advanced topics, preparing readers to apply GPT models responsibly and effectively in real-world systems. Ultimately, this book is more than a technical manual it is an invitation to experiment, question, and innovate. By demystifying GPT models and emphasizing reproducible, well-documented implementations, it aims to equip readers with the skills and insight necessary to contribute meaningfully to the evolving landscape of generative artificial intelligence
Artificial Intelligence Model Study Development And Integration Into The Information System
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Author : Moreau
language : en
Publisher: Moreau
Release Date : 2025-08-08
Artificial Intelligence Model Study Development And Integration Into The Information System written by Moreau and has been published by Moreau this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-08 with Computers categories.
Artificial Intelligence Model. Study, development, and integration into the information system.
Deep Learning Frameworks
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Author : Jamal Hopper
language : en
Publisher: Publifye AS
Release Date : 2025-02-18
Deep Learning Frameworks written by Jamal Hopper and has been published by Publifye AS this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-18 with Computers categories.
Deep Learning Frameworks are essential tools for developers and researchers in the rapidly advancing field of Artificial Intelligence. This book serves as a practical guide, providing a detailed exploration of deep learning frameworks like TensorFlow and PyTorch, and their underlying neural network architectures. Understanding these frameworks is vital for developing effective AI solutions, allowing for optimized resource allocation and efficient problem-solving. Did you know that different frameworks excel in different areas? For example, one might be better suited for image recognition while another shines in natural language processing. The book emphasizes practical application, bridging the gap between theoretical understanding and real-world implementation. It begins with fundamental concepts and a comparison of TensorFlow and PyTorch, highlighting their strengths and weaknesses. The book then progresses through various neural network architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), before concluding with advanced topics like model optimization and deployment strategies. This comprehensive approach ensures readers gain a solid foundation in AI development.
Deep Learning In Medical Signal And Image Processing
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Author : Aamir, Muhammad
language : en
Publisher: IGI Global
Release Date : 2025-05-23
Deep Learning In Medical Signal And Image Processing written by Aamir, Muhammad and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-23 with Computers categories.
Deep learning is revolutionizing the analysis of medical signals and images, offering unprecedented advancements in diagnostic accuracy and efficiency. Techniques such as convolutional and recurrent neural networks are transforming the processing of radiological scans, ultrasound images, and ECG readings. By enabling more detailed and precise interpretations, deep learning enhances the ability of healthcare providers to make timely and informed decisions. These innovations are reshaping medical workflows, improving patient outcomes, and paving the way for a future of more reliable and efficient healthcare solutions. Deep Learning in Medical Signal and Image Processing offers a comprehensive examination of deep learning, specifically through convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to medical data. It explores the application of AI in the analysis of medical signals and images. Covering topics such as diagnostic accuracy, enhanced decision-making, and data augmentation techniques, this book is an excellent resource for medical practitioners, clinicians, data scientists, AI researchers, healthcare professionals, engineers, professionals, researchers, scholars, academicians, and more.
Machine Learning
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Author : Satheesh Prabhu Gurusamy , Anil Kumar Veeramachaneni
language : en
Publisher: RK Publication
Release Date : 2025-06-14
Machine Learning written by Satheesh Prabhu Gurusamy , Anil Kumar Veeramachaneni and has been published by RK Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-14 with Computers categories.
This book offers a comprehensive introduction to Machine Learning, covering essential algorithms, data preprocessing, model evaluation, and real-world applications. It bridges theoretical concepts with practical implementations, making it ideal for students, researchers, and professionals aiming to harness the power of intelligent systems in diverse fields.
Scalable Deep Learning In Practice
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Author : Dr Adrian Devlin
language : en
Publisher: Independently Published
Release Date : 2025-11-14
Scalable Deep Learning In Practice written by Dr Adrian Devlin and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-14 with Computers categories.
Unlock the Power of Deep Learning-No Experience Needed! Are you fascinated by artificial intelligence, but intimidated by complex code, jargon, or the fear of making mistakes? Do you wish for a patient guide to walk you step-by-step through modern machine learning-without assuming you're already an expert? You're not alone, and you're in the right place. Scalable Deep Learning in Practice is your welcoming companion on the journey from beginner to confident deep learning practitioner. This hands-on book is designed for readers just like you: complete beginners, self-taught learners, and curious coders eager to turn ambition into real-world AI skills. You don't need a technical background to succeed. Every chapter breaks down intimidating concepts into clear, friendly explanations and walks you through building, training, and deploying powerful neural networks using PyTorch Lightning and ONNX. Each lesson celebrates small victories and normalizes mistakes, because learning should be encouraging, not overwhelming. Inside this book, you'll discover: Step-by-Step Guidance: Start from zero and build up-install tools, write your first Python code, and create your own deep learning models with confidence. Practical Projects: Work through real image and text classification projects, see your code in action, and develop skills you can use right away. Modern, Scalable Workflows: Learn how to train models faster, track experiments, and deploy your solutions anywhere-from your laptop to the cloud-using industry-leading tools. Mistakes Are Welcome: Enjoy a supportive learning environment where confusion is normal and every "aha!" moment is worth celebrating. Real-World Applications: Go beyond theory-discover how to take your models from idea to production with clear instructions on exporting to ONNX and serving with FastAPI. Personal Insights & Encouragement: Benefit from honest stories, troubleshooting tips, and practical advice from someone who knows what it's like to start from scratch. Whether you dream of building your own AI apps, landing a job in data science, or simply understanding what's behind today's smartest technologies, this book is your empowering first step. Key topics covered include: Deep learning for beginners PyTorch Lightning tutorial ONNX model deployment Reproducible machine learning workflows FastAPI and real-world ML API deployment Scaling projects for speed and reliability No experience? No problem! Open the first page, follow along at your own pace, and watch your confidence grow as you master the essentials of scalable deep learning. Ready to unlock your AI potential? Start your learning adventure today with a book that's as supportive as it is practical-your journey to real-world deep learning begins right here.
Shaping The Future Of Iot With Edge Intelligence
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Author : Rute C. Sofia
language : en
Publisher: CRC Press
Release Date : 2024-01-08
Shaping The Future Of Iot With Edge Intelligence written by Rute C. Sofia and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-08 with Computers categories.
This book presents the technologies that empower edge intelligence, along with their use in novel IoT solutions. Specifically, it presents how 5G/6G, Edge AI, and Blockchain solutions enable novel IoT-based decentralized intelligence use cases at the edge of the cloud/edge/IoT continuum. Emphasis is placed on presenting how these technologies support a wide array of functional and non-functional requirements spanning latency, performance, cybersecurity, data protection, real-time performance, energy efficiency, and more. The various chapters of the book are contributed by several EU-funded projects, which have recently developed novel IoT platforms that enable the development and deployment of edge intelligence applications based on the cloud/edge paradigm. Each one of the projects employs its own approach and uses a different mix of networking, middleware, and IoT technologies. Therefore, each of the chapters of the book contributes a unique perspective on the capabilities of enabling technologies and their integration in practical real-life applications in different sectors. The book is structured in five distinct parts. Each one of the first four parts focuses on a specific set of enabling technologies for edge intelligence and smart IoT applications in the cloud/edge/IoT continuum. Furthermore, the fifth part provides information about complementary aspects of next-generation IoT technology, including information about business models and IoT skills. Specifically: The first part focuses on 5G/6G networking technologies and their roles in implementing edge intelligence applications. The second part presents IoT applications that employ machine learning and other forms of Artificial Intelligence at the edge of the network. The third part illustrates decentralized IoT applications based on distributed ledger technologies. The fourth part is devoted to the presentation of novel IoT applications and use cases spanning the cloud/edge/IoT continuum. The fifth part discusses complementary aspects of IoT technologies, including business models and digital skills. The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons [Attribution-Non-Commercial (CC-BY-NC)] 4.0 license.
Intermediate Python And Large Language Models
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Author : Dilyan Grigorov
language : en
Publisher: Springer Nature
Release Date : 2025-06-27
Intermediate Python And Large Language Models written by Dilyan Grigorov and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-27 with Computers categories.
Harness the power of Large Language Models (LLMs) to build cutting-edge AI applications with Python and LangChain. This book provides a hands-on approach to understanding, implementing, and deploying LLM-powered solutions, equipping developers, data scientists, and AI enthusiasts with the tools to create real-world AI applications. The journey begins with an introduction to LangChain, covering its core concepts, integration with Python, and essential components such as prompt engineering, memory management, and retrieval-augmented generation (RAG). As you progress, you’ll explore advanced AI workflows, including multi-agent architectures, fine-tuning strategies, and optimization techniques to maximize LLM efficiency. The book also takes a deep dive into practical applications of LLMs, guiding you through the development of intelligent chatbots, document retrieval systems, content generation pipelines, and AI-driven automation tools. You’ll learn how to leverage APIs, integrate LLMs into web and mobile platforms, and optimize large-scale deployments while addressing key challenges such as inference latency, cost efficiency, and ethical considerations. By the end of the book, you’ll have gained a solid understanding of LLM architectures, hands-on experience with LangChain, and the expertise to build scalable AI applications that redefine human-computer interaction. What You Will Learn Understand the fundamentals of LangChain and Python for LLM development Know advanced AI workflows, including fine-tuning and memory management Build AI-powered applications such as chatbots, retrieval systems, and automation tools Know deployment strategies and performance optimization for real-world use Use best practices for scalability, security, and responsible AI implementation Unlock the full potential of LLMs and take your AI development skills to the next level Who This Book Is For Software engineers and Python developers interested in learning the foundations of LLMs and building advanced modern LLM applications for various tasks