Building Data Science Applications With Fastapi

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Building Data Science Applications with FastAPI

Author : Francois Voron
Publisher : Packt Publishing Ltd
ISBN 13 : 1801074186
Total Pages : 426 pages
Book Rating : 4.8/5 (1 download)

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Book Synopsis Building Data Science Applications with FastAPI by : Francois Voron

Download or read book Building Data Science Applications with FastAPI written by Francois Voron and published by Packt Publishing Ltd. This book was released on 2021-10-08 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 - Second Edition

Author : François Voron
Publisher :
ISBN 13 : 9781837632749
Total Pages : 0 pages
Book Rating : 4.6/5 (327 download)

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Book Synopsis Building Data Science Applications with FastAPI - Second Edition by : François Voron

Download or read book Building Data Science Applications with FastAPI - Second Edition written by François Voron and published by . This book was released on 2023-07-31 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 eBookKey FeaturesUncover the secrets of FastAPI, including async I/O, type hinting, and dependency injectionLearn to add authentication, authorization, and interaction with databases in a FastAPI backendDevelop real-world projects using pre-trained AI modelsBook DescriptionBuilding 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 learnExplore the basics of modern Python and async I/O programmingGet to grips with basic and advanced concepts of the FastAPI frameworkDeploy a performant and reliable web backend for a data science applicationIntegrate common Python data science libraries into a web backendIntegrate an object detection algorithm into a FastAPI backendBuild a distributed text-to-image AI system with Stable DiffusionAdd metrics and logging and learn how to monitor themWho this book is forThis 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

Author : Francois Voron
Publisher : Packt Publishing Ltd
ISBN 13 : 1837637261
Total Pages : 423 pages
Book Rating : 4.8/5 (376 download)

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Book Synopsis Building Data Science Applications with FastAPI by : Francois Voron

Download or read book Building Data Science Applications with FastAPI written by Francois Voron and published by Packt Publishing Ltd. This book was released on 2023-07-31 with total page 423 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Learn Python by Building Data Science Applications

Author : Philipp Kats
Publisher : Packt Publishing Ltd
ISBN 13 : 1789533066
Total Pages : 464 pages
Book Rating : 4.7/5 (895 download)

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Book Synopsis Learn Python by Building Data Science Applications by : Philipp Kats

Download or read book Learn Python by Building Data Science Applications written by Philipp Kats and published by Packt Publishing Ltd. This book was released on 2019-08-30 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Getting Started with Streamlit for Data Science

Author : Tyler Richards
Publisher : Packt Publishing Ltd
ISBN 13 : 1800563205
Total Pages : 282 pages
Book Rating : 4.8/5 (5 download)

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Book Synopsis Getting Started with Streamlit for Data Science by : Tyler Richards

Download or read book Getting Started with Streamlit for Data Science written by Tyler Richards and published by Packt Publishing Ltd. This book was released on 2021-08-20 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: Create, deploy, and test your Python applications, analyses, and models with ease using Streamlit Key Features Learn how to showcase machine learning models in a Streamlit application effectively and efficiently Become an expert Streamlit creator by getting hands-on with complex application creation Discover how Streamlit enables you to create and deploy apps effortlessly Book DescriptionStreamlit shortens the development time for the creation of data-focused web applications, allowing data scientists to create web app prototypes using Python in hours instead of days. Getting Started with Streamlit for Data Science takes a hands-on approach to helping you learn the tips and tricks that will have you up and running with Streamlit in no time. You'll start with the fundamentals of Streamlit by creating a basic app and gradually build on the foundation by producing high-quality graphics with data visualization and testing machine learning models. As you advance through the chapters, you’ll walk through practical examples of both personal data projects and work-related data-focused web applications, and get to grips with more challenging topics such as using Streamlit Components, beautifying your apps, and quick deployment of your new apps. By the end of this book, you’ll be able to create dynamic web apps in Streamlit quickly and effortlessly using the power of Python.What you will learn Set up your first development environment and create a basic Streamlit app from scratch Explore methods for uploading, downloading, and manipulating data in Streamlit apps Create dynamic visualizations in Streamlit using built-in and imported Python libraries Discover strategies for creating and deploying machine learning models in Streamlit Use Streamlit sharing for one-click deployment Beautify Streamlit apps using themes, Streamlit Components, and Streamlit sidebar Implement best practices for prototyping your data science work with Streamlit Who this book is for This book is for data scientists and machine learning enthusiasts who want to create web apps using Streamlit. Whether you’re a junior data scientist looking to deploy your first machine learning project in Python to improve your resume or a senior data scientist who wants to use Streamlit to make convincing and dynamic data analyses, this book will help you get there! Prior knowledge of Python programming will assist with understanding the concepts covered.

Building AI Applications with ChatGPT APIs

Author : Martin Yanev
Publisher : Packt Publishing Ltd
ISBN 13 : 1805128604
Total Pages : 258 pages
Book Rating : 4.8/5 (51 download)

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Book Synopsis Building AI Applications with ChatGPT APIs by : Martin Yanev

Download or read book Building AI Applications with ChatGPT APIs written by Martin Yanev and published by Packt Publishing Ltd. This book was released on 2023-09-21 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Author : Sinan Ozdemir
Publisher : Packt Publishing Ltd
ISBN 13 : 1837636001
Total Pages : 326 pages
Book Rating : 4.8/5 (376 download)

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Book Synopsis Principles of Data Science by : Sinan Ozdemir

Download or read book Principles of Data Science written by Sinan Ozdemir and published by Packt Publishing Ltd. This book was released on 2024-01-31 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Learn Python Programming

Author : Fabrizio Romano
Publisher : Packt Publishing Ltd
ISBN 13 : 1801815526
Total Pages : 555 pages
Book Rating : 4.8/5 (18 download)

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Book Synopsis Learn Python Programming by : Fabrizio Romano

Download or read book Learn Python Programming written by Fabrizio Romano and published by Packt Publishing Ltd. This book was released on 2021-10-29 with total page 555 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get up and running with Python 3.9 through concise tutorials and practical projects in this fully updated third edition. Purchase of the print or Kindle book includes a free eBook in PDF format. Key FeaturesExtensively revised with richer examples, Python 3.9 syntax, and new chapters on APIs and packaging and distributing Python codeDiscover how to think like a Python programmerLearn the fundamentals of Python through real-world projects in API development, GUI programming, and data scienceBook Description Learn Python Programming, Third Edition is both a theoretical and practical introduction to Python, an extremely flexible and powerful programming language that can be applied to many disciplines. This book will make learning Python easy and give you a thorough understanding of the language. You'll learn how to write programs, build modern APIs, and work with data by using renowned Python data science libraries. This revised edition covers the latest updates on API management, packaging applications, and testing. There is also broader coverage of context managers and an updated data science chapter. The book empowers you to take ownership of writing your software and become independent in fetching the resources you need. You will have a clear idea of where to go and how to build on what you have learned from the book. Through examples, the book explores a wide range of applications and concludes by building real-world Python projects based on the concepts you have learned. What you will learnGet Python up and running on Windows, Mac, and LinuxWrite elegant, reusable, and efficient code in any situationAvoid common pitfalls like duplication, complicated design, and over-engineeringUnderstand when to use the functional or object-oriented approach to programmingBuild a simple API with FastAPI and program GUI applications with TkinterGet an initial overview of more complex topics such as data persistence and cryptographyFetch, clean, and manipulate data, making efficient use of Python's built-in data structuresWho this book is for This book is for everyone who wants to learn Python from scratch, as well as experienced programmers looking for a reference book. Prior knowledge of basic programming concepts will help you follow along, but it's not a prerequisite.

Building Serverless Applications with Python

Author : Jalem Raj Rohit
Publisher : Packt Publishing Ltd
ISBN 13 : 1787281132
Total Pages : 266 pages
Book Rating : 4.7/5 (872 download)

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Book Synopsis Building Serverless Applications with Python by : Jalem Raj Rohit

Download or read book Building Serverless Applications with Python written by Jalem Raj Rohit and published by Packt Publishing Ltd. This book was released on 2018-04-20 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: Building efficient Python applications at minimal cost by adopting serverless architectures Key Features Design and set up a data flow between cloud services and custom business logic Make your applications efficient and reliable using serverless architecture Build and deploy scalable serverless Python APIs Book Description Serverless architectures allow you to build and run applications and services without having to manage the infrastructure. Many companies have adopted this architecture to save cost and improve scalability. This book will help you design serverless architectures for your applications with AWS and Python. The book is divided into three modules. The first module explains the fundamentals of serverless architecture and how AWS lambda functions work. In the next module, you will learn to build, release, and deploy your application to production. You will also learn to log and test your application. In the third module, we will take you through advanced topics such as building a serverless API for your application. You will also learn to troubleshoot and monitor your app and master AWS lambda programming concepts with API references. Moving on, you will also learn how to scale up serverless applications and handle distributed serverless systems in production. By the end of the book, you will be equipped with the knowledge required to build scalable and cost-efficient Python applications with a serverless framework. What you will learn Understand how AWS Lambda and Microsoft Azure Functions work and use them to create an application Explore various triggers and how to select them, based on the problem statement Build deployment packages for Lambda functions Master the finer details about building Lambda functions and versioning Log and monitor serverless applications Learn about security in AWS and Lambda functions Scale up serverless applications to handle huge workloads and serverless distributed systems in production Understand SAM model deployment in AWS Lambda Who this book is for This book is for Python developers who would like to learn about serverless architecture. Python programming knowledge is assumed.

Building Python Web APIs with FastAPI

Author : Abdulazeez Abdulazeez Adeshina
Publisher : Packt Publishing Ltd
ISBN 13 : 1801074518
Total Pages : 216 pages
Book Rating : 4.8/5 (1 download)

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Book Synopsis Building Python Web APIs with FastAPI by : Abdulazeez Abdulazeez Adeshina

Download or read book Building Python Web APIs with FastAPI written by Abdulazeez Abdulazeez Adeshina and published by Packt Publishing Ltd. This book was released on 2022-07-29 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover FastAPI features and best practices for building and deploying high-quality web APIs from scratch Key Features • A practical guide to developing production-ready web APIs rapidly in Python • Learn how to put FastAPI into practice by implementing it in real-world scenarios • Explore FastAPI, its syntax, and configurations for deploying applications Book Description RESTful web services are commonly used to create APIs for web-based applications owing to their light weight and high scalability. This book will show you how FastAPI, a high-performance web framework for building RESTful APIs in Python, allows you to build robust web APIs that are simple and intuitive and makes it easy to build quickly with very little boilerplate code. This book will help you set up a FastAPI application in no time and show you how to use FastAPI to build a REST API that receives and responds to user requests. You'll go on to learn how to handle routing and authentication while working with databases in a FastAPI application. The book walks you through the four key areas: building and using routes for create, read, update, and delete (CRUD) operations; connecting the application to SQL and NoSQL databases; securing the application built; and deploying your application locally or to a cloud environment. By the end of this book, you'll have developed a solid understanding of the FastAPI framework and be able to build and deploy robust REST APIs. What you will learn • Set up a FastAPI application that is fully functional and secure • Understand how to handle errors from requests and send proper responses in FastAPI • Integrate and connect your application to a SQL and NoSQL (MongoDB) database • Perform CRUD operations using SQL and FastAPI • Manage concurrency in FastAPI applications • Implement authentication in a FastAPI application • Deploy a FastAPI application to any platform Who this book is for This book is for Python developers who want to learn FastAPI in a pragmatic way to create robust web APIs with ease. If you are a Django or Flask developer looking to try something new that's faster, more efficient, and produces fewer bugs, this FastAPI Python book is for you. The book assumes intermediate-level knowledge of Python programming.

Deep Learning for Coders with fastai and PyTorch

Author : Jeremy Howard
Publisher : O'Reilly Media
ISBN 13 : 1492045497
Total Pages : 624 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Deep Learning for Coders with fastai and PyTorch by : Jeremy Howard

Download or read book Deep Learning for Coders with fastai and PyTorch written by Jeremy Howard and published by O'Reilly Media. This book was released on 2020-06-29 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Building Python Microservices with FastAPI

Author : Sherwin John C. Tragura
Publisher : Packt Publishing Ltd
ISBN 13 : 1803238798
Total Pages : 420 pages
Book Rating : 4.8/5 (32 download)

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Book Synopsis Building Python Microservices with FastAPI by : Sherwin John C. Tragura

Download or read book Building Python Microservices with FastAPI written by Sherwin John C. Tragura and published by Packt Publishing Ltd. This book was released on 2022-08-30 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the secrets of building Python microservices using the FastAPI framework Key Features Provides a reference that contains definitions, illustrations, comparative analysis, and the implementation of real-world apps Covers concepts, core details, and advanced integration and design-related topics Imparts context, app templates, suggestions, and insights that are helpful to actual projects Book Description FastAPI is an Asynchronous Server Gateway Interface (ASGI)-based framework that can help build modern, manageable, and fast microservices. Because of its asynchronous core platform, this ASGI-based framework provides the best option when it comes to performance, reliability, and scalability over the WSGI-based Django and Flask. When working with Python, Flask, and Django microservices, you'll be able to put your knowledge to work with this practical guide to building seamlessly manageable and fast microservices. You'll begin by understanding the background of FastAPI and learning how to install, configure, and use FastAPI to decompose business units. You'll explore a unique and asynchronous REST API framework that can provide a better option when it comes to building microservices. After that, this book will guide you on how to apply and translate microservices design patterns in building various microservices applications and RESTful APIs using the FastAPI framework. By the end of this microservices book, you'll be able to understand, build, deploy, test, and experiment with microservices and their components using the FastAPI framework. What you will learn Understand, orient, and implement REST APIs using the basic components of the FastAPI framework Build asynchronous as well as synchronous REST services using the built-in pydantic module and asyncio support Create small-scale and large-scale microservices applications using features supported by FastAPI Build event-driven and message-driven applications using the framework Create an asynchronous and synchronous data layer with both relational and NoSQL databases Perform numerical and symbolic computations with FastAPI Who this book is for This book is for Python web developers, advanced Python developers, and backend developers using Flask or Django who want to learn how to use the FastAPI framework to implement microservices. Readers familiar with the REST API and microservices will also benefit from this book. Some parts of the book contain general concepts, processes, and instructions that intermediate-level developers and Python enthusiasts can relate to as well.

The Python Apprentice

Author : Robert Smallshire
Publisher : Packt Publishing Ltd
ISBN 13 : 1788298667
Total Pages : 344 pages
Book Rating : 4.7/5 (882 download)

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Book Synopsis The Python Apprentice by : Robert Smallshire

Download or read book The Python Apprentice written by Robert Smallshire and published by Packt Publishing Ltd. This book was released on 2017-06-21 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn the Python skills and culture you need to become a productive member of any Python project. About This Book Taking a practical approach to studying Python A clear appreciation of the sequence-oriented parts of Python Emphasis on the way in which Python code is structured Learn how to produce bug-free code by using testing tools Who This Book Is For The Python Apprentice is for anyone who wants to start building, creating and contributing towards a Python project. No previous knowledge of Python is required, although at least some familiarity with programming in another language is helpful. What You Will Learn Learn the language of Python itself Get a start on the Python standard library Learn how to integrate 3rd party libraries Develop libraries on your own Become familiar with the basics of Python testing In Detail Experienced programmers want to know how to enhance their craft and we want to help them start as apprentices with Python. We know that before mastering Python you need to learn the culture and the tools to become a productive member of any Python project. Our goal with this book is to give you a practical and thorough introduction to Python programming, providing you with the insight and technical craftsmanship you need to be a productive member of any Python project. Python is a big language, and it's not our intention with this book to cover everything there is to know. We just want to make sure that you, as the developer, know the tools, basic idioms and of course the ins and outs of the language, the standard library and other modules to be able to jump into most projects. Style and approach We introduce topics gently and then revisit them on multiple occasions to add the depth required to support your progression as a Python developer. We've worked hard to structure the syllabus to avoid forward references. On only a few occasions do we require you to accept techniques on trust, before explaining them later; where we do, it's to deliberately establish good habits.

Modern Computer Vision with PyTorch

Author : V Kishore Ayyadevara
Publisher : Packt Publishing Ltd
ISBN 13 : 1839216530
Total Pages : 805 pages
Book Rating : 4.8/5 (392 download)

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Book Synopsis Modern Computer Vision with PyTorch by : V Kishore Ayyadevara

Download or read book Modern Computer Vision with PyTorch written by V Kishore Ayyadevara and published by Packt Publishing Ltd. This book was released on 2020-11-27 with total page 805 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get to grips with deep learning techniques for building image processing applications using PyTorch with the help of code notebooks and test questions Key FeaturesImplement solutions to 50 real-world computer vision applications using PyTorchUnderstand the theory and working mechanisms of neural network architectures and their implementationDiscover best practices using a custom library created especially for this bookBook Description Deep learning is the driving force behind many recent advances in various computer vision (CV) applications. This book takes a hands-on approach to help you to solve over 50 CV problems using PyTorch1.x on real-world datasets. You’ll start by building a neural network (NN) from scratch using NumPy and PyTorch and discover best practices for tweaking its hyperparameters. You’ll then perform image classification using convolutional neural networks and transfer learning and understand how they work. As you progress, you’ll implement multiple use cases of 2D and 3D multi-object detection, segmentation, human-pose-estimation by learning about the R-CNN family, SSD, YOLO, U-Net architectures, and the Detectron2 platform. The book will also guide you in performing facial expression swapping, generating new faces, and manipulating facial expressions as you explore autoencoders and modern generative adversarial networks. You’ll learn how to combine CV with NLP techniques, such as LSTM and transformer, and RL techniques, such as Deep Q-learning, to implement OCR, image captioning, object detection, and a self-driving car agent. Finally, you'll move your NN model to production on the AWS Cloud. By the end of this book, you’ll be able to leverage modern NN architectures to solve over 50 real-world CV problems confidently. What you will learnTrain a NN from scratch with NumPy and PyTorchImplement 2D and 3D multi-object detection and segmentationGenerate digits and DeepFakes with autoencoders and advanced GANsManipulate images using CycleGAN, Pix2PixGAN, StyleGAN2, and SRGANCombine CV with NLP to perform OCR, image captioning, and object detectionCombine CV with reinforcement learning to build agents that play pong and self-drive a carDeploy a deep learning model on the AWS server using FastAPI and DockerImplement over 35 NN architectures and common OpenCV utilitiesWho this book is for This book is for beginners to PyTorch and intermediate-level machine learning practitioners who are looking to get well-versed with computer vision techniques using deep learning and PyTorch. If you are just getting started with neural networks, you’ll find the use cases accompanied by notebooks in GitHub present in this book useful. Basic knowledge of the Python programming language and machine learning is all you need to get started with this book.

Head First Go

Author : Jay McGavren
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491969504
Total Pages : 558 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Head First Go by : Jay McGavren

Download or read book Head First Go written by Jay McGavren and published by "O'Reilly Media, Inc.". This book was released on 2019-04-04 with total page 558 pages. Available in PDF, EPUB and Kindle. Book excerpt: What will you learn from this book? Go makes it easy to build software that’s simple, reliable, and efficient. Andthis book makes it easy for programmers like you to get started. Googledesigned Go for high-performance networking and multiprocessing, but—like Python and JavaScript—the language is easy to read and use. With thispractical hands-on guide, you’ll learn how to write Go code using clearexamples that demonstrate the language in action. Best of all, you’ll understandthe conventions and techniques that employers want entry-level Godevelopers to know. Why does this book look so different? Based on the latest research in cognitive science and learning theory, HeadFirst Go uses a visually rich format to engage your mind rather than a textheavyapproach that puts you to sleep. Why waste your time struggling withnew concepts? This multisensory learning experience is designed for theway your brain really works.

Statistics with Julia

Author : Yoni Nazarathy
Publisher : Springer Nature
ISBN 13 : 3030709019
Total Pages : 527 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Statistics with Julia by : Yoni Nazarathy

Download or read book Statistics with Julia written by Yoni Nazarathy and published by Springer Nature. This book was released on 2021-09-04 with total page 527 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph uses the Julia language to guide the reader through an exploration of the fundamental concepts of probability and statistics, all with a view of mastering machine learning, data science, and artificial intelligence. The text does not require any prior statistical knowledge and only assumes a basic understanding of programming and mathematical notation. It is accessible to practitioners and researchers in data science, machine learning, bio-statistics, finance, or engineering who may wish to solidify their knowledge of probability and statistics. The book progresses through ten independent chapters starting with an introduction of Julia, and moving through basic probability, distributions, statistical inference, regression analysis, machine learning methods, and the use of Monte Carlo simulation for dynamic stochastic models. Ultimately this text introduces the Julia programming language as a computational tool, uniquely addressing end-users rather than developers. It makes heavy use of over 200 code examples to illustrate dozens of key statistical concepts. The Julia code, written in a simple format with parameters that can be easily modified, is also available for download from the book’s associated GitHub repository online. See what co-creators of the Julia language are saying about the book: Professor Alan Edelman, MIT: With “Statistics with Julia”, Yoni and Hayden have written an easy to read, well organized, modern introduction to statistics. The code may be looked at, and understood on the static pages of a book, or even better, when running live on a computer. Everything you need is here in one nicely written self-contained reference. Dr. Viral Shah, CEO of Julia Computing: Yoni and Hayden provide a modern way to learn statistics with the Julia programming language. This book has been perfected through iteration over several semesters in the classroom. It prepares the reader with two complementary skills - statistical reasoning with hands on experience and working with large datasets through training in Julia.

Machine Learning Engineering with MLflow

Author : Natu Lauchande
Publisher : Packt Publishing Ltd
ISBN 13 : 1800561695
Total Pages : 249 pages
Book Rating : 4.8/5 (5 download)

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Book Synopsis Machine Learning Engineering with MLflow by : Natu Lauchande

Download or read book Machine Learning Engineering with MLflow written by Natu Lauchande and published by Packt Publishing Ltd. This book was released on 2021-08-27 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get up and running, and productive in no time with MLflow using the most effective machine learning engineering approach Key FeaturesExplore machine learning workflows for stating ML problems in a concise and clear manner using MLflowUse MLflow to iteratively develop a ML model and manage it Discover and work with the features available in MLflow to seamlessly take a model from the development phase to a production environmentBook Description MLflow is a platform for the machine learning life cycle that enables structured development and iteration of machine learning models and a seamless transition into scalable production environments. This book will take you through the different features of MLflow and how you can implement them in your ML project. You will begin by framing an ML problem and then transform your solution with MLflow, adding a workbench environment, training infrastructure, data management, model management, experimentation, and state-of-the-art ML deployment techniques on the cloud and premises. The book also explores techniques to scale up your workflow as well as performance monitoring techniques. As you progress, you'll discover how to create an operational dashboard to manage machine learning systems. Later, you will learn how you can use MLflow in the AutoML, anomaly detection, and deep learning context with the help of use cases. In addition to this, you will understand how to use machine learning platforms for local development as well as for cloud and managed environments. This book will also show you how to use MLflow in non-Python-based languages such as R and Java, along with covering approaches to extend MLflow with Plugins. By the end of this machine learning book, you will be able to produce and deploy reliable machine learning algorithms using MLflow in multiple environments. What you will learnDevelop your machine learning project locally with MLflow's different featuresSet up a centralized MLflow tracking server to manage multiple MLflow experimentsCreate a model life cycle with MLflow by creating custom modelsUse feature streams to log model results with MLflowDevelop the complete training pipeline infrastructure using MLflow featuresSet up an inference-based API pipeline and batch pipeline in MLflowScale large volumes of data by integrating MLflow with high-performance big data librariesWho this book is for This book is for data scientists, machine learning engineers, and data engineers who want to gain hands-on machine learning engineering experience and learn how they can manage an end-to-end machine learning life cycle with the help of MLflow. Intermediate-level knowledge of the Python programming language is expected.