Local cover image
Local cover image
Tipo: materialTypeLabelLibro - General
Ubicación Física: 005.133 / V954 2021

Building data science applications with fastAPI : develop, manage, and deploy efficient machine learning applications with Python /

Autor: Voron, François.
Otros Autores: Guerreiro, Izabela dos Santos ( revisor ) ; Becker, Richard ( revisor ) ; Jamir Silva, William ( revisor ) .
Pié de imprenta: 2021
Pié de imprenta: Birmingham (Inglaterra) : Packt Publishing, 2021.
Descripción: 407 páginas : Ilustraciones, gráficas, fotografías en blanco y negro ; 19 x 23 cm.
ISBN: 9781801079211.
Tema(s):
Contenido: Introduction to Python and fastAPI. 1.Python Development enviorement setup. 2.Python programming specificities. 3.Developing a RESTful API with FastAPI. 4.Managing pydantic data models in FastAPI. 5.Dependency injections in FastAPI. Build and deploy a complete web backend with FastAPI. 6.Database and Asynchronous ORMs. 7.Managing authentication and security in FastAPI. 8. Defining WebSockets for Two-Way Interactive Communication in FastAPI. 9.Testing an API Asynchronously with pytest and HTTPX. 10. Deploying a FastAPI Project. Build a data sciencie API with Python and FastAPI. 11. Introduction to NumPy and Pandas. 12. Training Machine Learning Models with scikit-learn. 13. Creating an Efficient Prediction API Endpoint with FastAPI. 14. Implement a Real-Time Face Detection System Using WebSockets with FastAPI and OpenCV. Index.
Resumen: 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. (Taken from the source).

List(s) this item appears in: Adquisiciones Ingeniería de Sistemas 2017-
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)

Introduction to Python and fastAPI. 1.Python Development enviorement setup. 2.Python programming specificities. 3.Developing a RESTful API with FastAPI. 4.Managing pydantic data models in FastAPI. 5.Dependency injections in FastAPI. Build and deploy a complete web backend with FastAPI. 6.Database and Asynchronous ORMs. 7.Managing authentication and security in FastAPI. 8. Defining WebSockets for Two-Way Interactive Communication in FastAPI. 9.Testing an API Asynchronously with pytest and HTTPX. 10. Deploying a FastAPI Project. Build a data sciencie API with Python and FastAPI. 11. Introduction to NumPy and Pandas. 12. Training Machine Learning Models with scikit-learn. 13. Creating an Efficient Prediction API Endpoint with FastAPI. 14. Implement a Real-Time Face Detection System Using WebSockets with FastAPI and OpenCV. Index.

Sistemas

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. (Taken from the source).

There are no comments on this title.

to post a comment.

Click on an image to view it in the image viewer

Local cover image

Universidad Católica de Colombia • PBX: (57 1) 3 27 73 00 - (57 1) 3 27 73 33
Bogotá, Avenida Caracas # 46 -72, sede Las Torres • Bogotá, Carrera 13 # 47 – 30, Sede 4​ • Bogotá, Diagonal 46 A # 15 B – 10, sede El Claustro
Bogotá, Carrera 13 # 47 – 49, sede Carrera 13