Building Data Science Applications With Fastapi
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Building Data Science Applications With Fastapi
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Author : Francois Voron
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-07-31
Building Data Science Applications With Fastapi written by Francois Voron 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 2023-07-31 with Computers categories.
Learn all the features and best practices of FastAPI to build, deploy, and monitor powerful data science and AI apps, like object detection or image generation. Purchase of the print or Kindle book includes a free PDF eBook Key Features Uncover the secrets of FastAPI, including async I/O, type hinting, and dependency injection Learn to add authentication, authorization, and interaction with databases in a FastAPI backend Develop real-world projects using pre-trained AI models Book Description Building Data Science Applications with FastAPI is the go-to resource for creating efficient and dependable data science API backends. This second edition incorporates the latest Python and FastAPI advancements, along with two new AI projects – a real-time object detection system and a text-to-image generation platform using Stable Diffusion. The book starts with the basics of FastAPI and modern Python programming. You'll grasp FastAPI's robust dependency injection system, which facilitates seamless database communication, authentication implementation, and ML model integration. As you progress, you'll learn testing and deployment best practices, guaranteeing high-quality, resilient applications. Throughout the book, you'll build data science applications using FastAPI with the help of projects covering common AI use cases, such as object detection and text-to-image generation. These hands-on experiences will deepen your understanding of using FastAPI in real-world scenarios. By the end of this book, you'll be well equipped to maintain, design, and monitor applications to meet the highest programming standards using FastAPI, empowering you to create fast and reliable data science API backends with ease while keeping up with the latest advancements. What you will learn Explore the basics of modern Python and async I/O programming Get to grips with basic and advanced concepts of the FastAPI framework Deploy a performant and reliable web backend for a data science application Integrate common Python data science libraries into a web backend Integrate an object detection algorithm into a FastAPI backend Build a distributed text-to-image AI system with Stable Diffusion Add metrics and logging and learn how to monitor them Who this book is for This book is for data scientists and software developers interested in gaining knowledge of FastAPI and its ecosystem to build data science applications. Basic knowledge of data science and machine learning concepts and how to apply them in Python is recommended.
Building Data Science Applications With Fastapi
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Author : Francois Voron
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-10-08
Building Data Science Applications With Fastapi written by Francois Voron 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 2021-10-08 with Computers categories.
Get well-versed with FastAPI features and best practices for testing, monitoring, and deployment to run high-quality and robust data science applications Key FeaturesCover the concepts of the FastAPI framework, including aspects relating to asynchronous programming, type hinting, and dependency injectionDevelop efficient RESTful APIs for data science with modern PythonBuild, test, and deploy high performing data science and machine learning systems with FastAPIBook Description FastAPI is a web framework for building APIs with Python 3.6 and its later versions based on standard Python-type hints. With this book, you'll be able to create fast and reliable data science API backends using practical examples. This book starts with the basics of the FastAPI framework and associated modern Python programming language concepts. You'll be taken through all the aspects of the framework, including its powerful dependency injection system and how you can use it to communicate with databases, implement authentication and integrate machine learning models. Later, you'll cover best practices relating to testing and deployment to run a high-quality and robust application. You'll also be introduced to the extensive ecosystem of Python data science packages. As you progress, you'll learn how to build data science applications in Python using FastAPI. The book also demonstrates how to develop fast and efficient machine learning prediction backends and test them to achieve the best performance. Finally, you'll see how to implement a real-time face detection system using WebSockets and a web browser as a client. By the end of this FastAPI book, you'll have not only learned how to implement Python in data science projects but also how to maintain and design them to meet high programming standards with the help of FastAPI. What you will learnExplore the basics of modern Python and async I/O programmingGet to grips with basic and advanced concepts of the FastAPI frameworkImplement a FastAPI dependency to efficiently run a machine learning modelIntegrate a simple face detection algorithm in a FastAPI backendIntegrate common Python data science libraries in a web backendDeploy a performant and reliable web backend for a data science applicationWho this book is for This Python data science book is for data scientists and software developers interested in gaining knowledge of FastAPI and its ecosystem to build data science applications. Basic knowledge of data science and machine learning concepts and how to apply them in Python is recommended.
Building Data Science Applications With Fastapi
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Author : Francois Voron
language : en
Publisher: Packt Publishing
Release Date : 2021-10-08
Building Data Science Applications With Fastapi written by Francois Voron and has been published by Packt Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-08 with categories.
Get well-versed with FastAPI features and best practices for testing, monitoring, and deployment to run high-quality and robust data science applications Key Features: Cover the concepts of the FastAPI framework, including aspects relating to asynchronous programming, type hinting, and dependency injection Develop efficient RESTful APIs for data science with modern Python Build, test, and deploy high performing data science and machine learning systems with FastAPI Book Description: FastAPI is a web framework for building APIs with Python 3.6 and its later versions based on standard Python-type hints. With this book, you'll be able to create fast and reliable data science API backends using practical examples. This book starts with the basics of the FastAPI framework and associated modern Python programming language concepts. You'll then be taken through all the aspects of the framework, including its powerful dependency injection system and how you can use it to communicate with databases, implement authentication and integrate machine learning models. Later, you'll cover best practices relating to testing and deployment to run a high-quality and robust application. You'll also be introduced to the extensive ecosystem of Python data science packages. As you progress, you'll learn how to build data science applications in Python using FastAPI. The book also demonstrates how to develop fast and efficient machine learning prediction backends and test them to achieve the best performance. Finally, you'll see how to implement a real-time face detection system using WebSockets and a web browser as a client. By the end of this FastAPI book, you'll have not only learned how to implement Python in data science projects but also how to maintain and design them to meet high programming standards with the help of FastAPI. What You Will Learn: Explore the basics of modern Python and async I/O programming Get to grips with basic and advanced concepts of the FastAPI framework Implement a FastAPI dependency to efficiently run a machine learning model Integrate a simple face detection algorithm in a FastAPI backend Integrate common Python data science libraries in a web backend Deploy a performant and reliable web backend for a data science application Who this book is for: This Python data science book is for data scientists and software developers interested in gaining knowledge of FastAPI and its ecosystem to build data science applications. Basic knowledge of data science and machine learning concepts and how to apply them in Python is recommended.
Learn Python By Building Data Science Applications
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Author : Philipp Kats
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-08-30
Learn Python By Building Data Science Applications written by Philipp Kats 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 2019-08-30 with Computers categories.
Understand the constructs of the Python programming language and use them to build data science projects Key FeaturesLearn the basics of developing applications with Python and deploy your first data applicationTake your first steps in Python programming by understanding and using data structures, variables, and loopsDelve into Jupyter, NumPy, Pandas, SciPy, and sklearn to explore the data science ecosystem in PythonBook Description Python is the most widely used programming language for building data science applications. Complete with step-by-step instructions, this book contains easy-to-follow tutorials to help you learn Python and develop real-world data science projects. The “secret sauce” of the book is its curated list of topics and solutions, put together using a range of real-world projects, covering initial data collection, data analysis, and production. This Python book starts by taking you through the basics of programming, right from variables and data types to classes and functions. You’ll learn how to write idiomatic code and test and debug it, and discover how you can create packages or use the range of built-in ones. You’ll also be introduced to the extensive ecosystem of Python data science packages, including NumPy, Pandas, scikit-learn, Altair, and Datashader. Furthermore, you’ll be able to perform data analysis, train models, and interpret and communicate the results. Finally, you’ll get to grips with structuring and scheduling scripts using Luigi and sharing your machine learning models with the world as a microservice. By the end of the book, you’ll have learned not only how to implement Python in data science projects, but also how to maintain and design them to meet high programming standards. What you will learnCode in Python using Jupyter and VS CodeExplore the basics of coding – loops, variables, functions, and classesDeploy continuous integration with Git, Bash, and DVCGet to grips with Pandas, NumPy, and scikit-learnPerform data visualization with Matplotlib, Altair, and DatashaderCreate a package out of your code using poetry and test it with PyTestMake your machine learning model accessible to anyone with the web APIWho this book is for If you want to learn Python or data science in a fun and engaging way, this book is for you. You’ll also find this book useful if you’re a high school student, researcher, analyst, or anyone with little or no coding experience with an interest in the subject and courage to learn, fail, and learn from failing. A basic understanding of how computers work will be useful.
Building Ai Applications With Chatgpt Apis
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Author : Martin Yanev
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-09-21
Building Ai Applications With Chatgpt Apis written by Martin Yanev 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 2023-09-21 with Computers categories.
Enhance your application development skills by building a ChatGPT clone, code bug fixer, quiz generator, translation app, email auto-reply, PowerPoint generator, and more in just one read! Key Features Become proficient in building AI applications with ChatGPT, DALL-E, and Whisper Understand how to select the optimal ChatGPT model and fine-tune it for your specific use case Monetize your applications by integrating the ChatGPT API with Stripe Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionCombining ChatGPT APIs with Python opens doors to building extraordinary AI applications. By leveraging these APIs, you can focus on the application logic and user experience, while ChatGPT’s robust NLP capabilities handle the intricacies of human-like text understanding and generation. This book is a guide for beginners to master the ChatGPT, Whisper, and DALL-E APIs by building ten innovative AI projects. These projects offer practical experience in integrating ChatGPT with frameworks and tools such as Flask, Django, Microsoft Office APIs, and PyQt. Throughout this book, you’ll get to grips with performing NLP tasks, building a ChatGPT clone, and creating an AI-driven code bug fixing SaaS application. You’ll also cover speech recognition, text-to-speech functionalities, language translation, and generation of email replies and PowerPoint presentations. This book teaches you how to fine-tune ChatGPT and generate AI art using DALL-E APIs, and then offers insights into selling your apps by integrating ChatGPT API with Stripe. With practical examples available on GitHub, the book gradually progresses from easy to advanced topics, cultivating the expertise required to develop, deploy, and monetize your own groundbreaking applications by harnessing the full potential of ChatGPT APIs.What you will learn Develop a solid foundation in using the ChatGPT API for natural language processing tasks Build, deploy, and capitalize on a variety of desktop and SaaS AI applications Seamlessly integrate ChatGPT with established frameworks such as Flask, Django, and Microsoft Office APIs Channel your creativity by integrating DALL-E APIs to produce stunning AI-generated art within your desktop applications Experience the power of Whisper API's speech recognition and text-to-speech features Discover techniques to optimize ChatGPT models through the process of fine-tuning Who this book is for With best practices, tips, and tricks for building applications using the ChatGPT API, this book is for programmers, entrepreneurs, and software enthusiasts. Python developers interested in AI applications involving ChatGPT, software developers who want to integrate AI technology, and web developers looking to create AI-powered web applications with ChatGPT will also find this book useful. A fundamental understanding of Python programming and experience of working with APIs will help you make the most of this book.
Principles Of Data Science
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Author : Sinan Ozdemir
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-01-31
Principles Of Data Science written by Sinan Ozdemir 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 2024-01-31 with Computers categories.
Transform your data into insights with must-know techniques and mathematical concepts to unravel the secrets hidden within your data Key Features Learn practical data science combined with data theory to gain maximum insights from data Discover methods for deploying actionable machine learning pipelines while mitigating biases in data and models Explore actionable case studies to put your new skills to use immediately Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionPrinciples of Data Science bridges mathematics, programming, and business analysis, empowering you to confidently pose and address complex data questions and construct effective machine learning pipelines. This book will equip you with the tools to transform abstract concepts and raw statistics into actionable insights. Starting with cleaning and preparation, you’ll explore effective data mining strategies and techniques before moving on to building a holistic picture of how every piece of the data science puzzle fits together. Throughout the book, you’ll discover statistical models with which you can control and navigate even the densest or the sparsest of datasets and learn how to create powerful visualizations that communicate the stories hidden in your data. With a focus on application, this edition covers advanced transfer learning and pre-trained models for NLP and vision tasks. You’ll get to grips with advanced techniques for mitigating algorithmic bias in data as well as models and addressing model and data drift. Finally, you’ll explore medium-level data governance, including data provenance, privacy, and deletion request handling. By the end of this data science book, you'll have learned the fundamentals of computational mathematics and statistics, all while navigating the intricacies of modern ML and large pre-trained models like GPT and BERT.What you will learn Master the fundamentals steps of data science through practical examples Bridge the gap between math and programming using advanced statistics and ML Harness probability, calculus, and models for effective data control Explore transformative modern ML with large language models Evaluate ML success with impactful metrics and MLOps Create compelling visuals that convey actionable insights Quantify and mitigate biases in data and ML models Who this book is for If you are an aspiring novice data scientist eager to expand your knowledge, this book is for you. Whether you have basic math skills and want to apply them in the field of data science, or you excel in programming but lack the necessary mathematical foundations, you’ll find this book useful. Familiarity with Python programming will further enhance your learning experience.
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
Advanced Machine Learning Ai And Cybersecurity In Web3 Theoretical Knowledge And Practical Application
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Author : Bouarara, Hadj Ahmed
language : en
Publisher: IGI Global
Release Date : 2024-08-23
Advanced Machine Learning Ai And Cybersecurity In Web3 Theoretical Knowledge And Practical Application written by Bouarara, Hadj Ahmed and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-23 with Computers categories.
In the evolving landscape of Web3, the use of advanced machine learning, artificial intelligence, and cybersecurity transforms industries through theoretical exploration and practical application. The integration of advanced machine learning and AI techniques promises enhanced security protocols, predictive analytics, and adaptive defenses against the increasing number of cyber threats. However, these technological improvements also raise questions regarding privacy, transparency, and the ethical implications of AI-driven security measures. Advanced Machine Learning, AI, and Cybersecurity in Web3: Theoretical Knowledge and Practical Application explores theories and applications of improved technological techniques in Web 3.0. It addresses the challenges inherent to decentralization while harnessing the benefits offered by advances, thereby paving the way for a safer and more advanced digital era. Covering topics such as fraud detection, cryptocurrency, and data management, this book is a useful resource for computer engineers, financial institutions, security and IT professionals, business owners, researchers, scientists, and academicians.
Interpretable Machine Learning With Python
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Author : Serg Masís
language : en
Publisher:
Release Date : 2021-03-26
Interpretable Machine Learning With Python written by Serg Masís and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-26 with categories.
Understand the key aspects and challenges of machine learning interpretability, learn how to overcome them with interpretation methods, and leverage them to build fairer, safer, and more reliable models Key Features: Learn how to extract easy-to-understand insights from any machine learning model Become well-versed with interpretability techniques to build fairer, safer, and more reliable models Mitigate risks in AI systems before they have broader implications by learning how to debug black-box models Book Description: Do you want to understand your models and mitigate risks associated with poor predictions using machine learning (ML) interpretation? Interpretable Machine Learning with Python can help you work effectively with ML models. The first section of the book is a beginner's guide to interpretability, covering its relevance in business and exploring its key aspects and challenges. You'll focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. The second section will get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, the book also helps the reader to interpret model outcomes using examples. In the third section, you'll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you'll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining. By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning. What You Will Learn: Recognize the importance of interpretability in business Study models that are intrinsically interpretable such as linear models, decision trees, and Naïve Bayes Become well-versed in interpreting models with model-agnostic methods Visualize how an image classifier works and what it learns Understand how to mitigate the influence of bias in datasets Discover how to make models more reliable with adversarial robustness Use monotonic constraints to make fairer and safer models Who this book is for: This book is for data scientists, machine learning developers, and data stewards who have an increasingly critical responsibility to explain how the AI systems they develop work, their impact on decision making, and how they identify and manage bias. Working knowledge of machine learning and the Python programming language is expected.
Microservice Apis
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Author : Jose Haro Peralta
language : en
Publisher: Simon and Schuster
Release Date : 2023-03-07
Microservice Apis written by Jose Haro Peralta 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 2023-03-07 with Computers categories.
Strategies, best practices, and patterns that will help you design resilient microservices architecture and streamline your API integrations. In Microservice APIs, you’ll discover: Service decomposition strategies for microservices Documentation-driven development for APIs Best practices for designing REST and GraphQL APIs Documenting REST APIs with the OpenAPI specification (formerly Swagger) Documenting GraphQL APIs using the Schema Definition Language Building microservices APIs with Flask, FastAPI, Ariadne, and other frameworks Service implementation patterns for loosely coupled services Property-based testing to validate your APIs, and using automated API testing frameworks like schemathesis and Dredd Adding authentication and authorization to your microservice APIs using OAuth and OpenID Connect (OIDC) Deploying and operating microservices in AWS with Docker and Kubernetes Microservice APIs teaches you practical techniques for designing robust microservices with APIs that are easy to understand, consume, and maintain. You’ll benefit from author José Haro Peralta’s years of experience experimenting with microservices architecture, dodging pitfalls and learning from mistakes he’s made. Inside you’ll find strategies for delivering successful API integrations, implementing services with clear boundaries, managing cloud deployments, and handling microservices security. Written in a framework-agnostic manner, its universal principles can easily be applied to your favorite stack and toolset. About the technology Clean, clear APIs are essential to the success of microservice applications. Well-designed APIs enable reliable integrations between services and help simplify maintenance, scaling, and redesigns. Th is book teaches you the patterns, protocols, and strategies you need to design, build, and deploy effective REST and GraphQL microservices APIs. About the book Microservice APIs gathers proven techniques for creating and building easy-to-consume APIs for microservices applications. Rich with proven advice and Python-based examples, this practical book focuses on implementation over philosophy. You’ll learn how to build robust microservice APIs, test and protect them, and deploy them to the cloud following principles and patterns that work in any language. What's inside Service decomposition strategies for microservices Best practices for designing and building REST and GraphQL APIs Service implementation patterns for loosely coupled components API authorization with OAuth and OIDC Deployments with AWS and Kubernetes About the reader For developers familiar with the basics of web development. Examples are in Python. About the author José Haro Peralta is a consultant, author, and instructor. He’s also the founder of microapis.io. Table of Contents PART 1 INTRODUCING MICROSERVICE APIS 1 What are microservice APIs? 2 A basic API implementation 3 Designing microservices PART 2 DESIGNING AND BUILDING REST APIS 4 Principles of REST API design 5 Documenting REST APIs with OpenAPI 6 Building REST APIs with Python 7 Service implementation patterns for microservices PART 3 DESIGNING AND BUILDING GRAPHQL APIS 8 Designing GraphQL APIs 9 Consuming GraphQL APIs 10 Building GraphQL APIs with Python PART 4 SECURING, TESTING, AND DEPLOYING MICROSERVICE APIS 11 API authorization and authentication 12 Testing and validating APIs 13 Dockerizing microservice APIs 14 Deploying microservice APIs with Kubernetes