Building Data Science Applications with FastAPI: Develop, manage, and deploy efficient machine learning applications with Python
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Building Data Science Applications with FastAPI: Develop, manage, and deploy efficient machine learning applications with Python
Build, test, and deploy high performing data science and machine learning systems with FastAPI
Building Data Science Applications with FastAPI: Develop, manage, and deploy efficient machine learning applications with Python
товар №: 51642211

Building Data Science Applications with FastAPI: Develop, manage, and deploy efficient machine learning applications with Python

90% of respondents would recommend this to a friend

товар №: 51642211

KGS 6910

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Build, test, and deploy high performing data science and machine learning systems with FastAPI
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What Stands Out

Rapid Development
Leverage FastAPI's intuitive framework for swift application development, enabling data scientists to quickly turn machine learning models into fully functional APIs with minimal effort.
Asynchronous Support
Utilize FastAPI's asynchronous capabilities to handle numerous requests simultaneously, ensuring scalable and efficient performance for data-driven applications without compromising speed.
Comprehensive Documentation
Benefit from extensive and user-friendly documentation that facilitates easy navigation and implementation, helping users effectively manage and deploy their machine learning projects with confidence.

Информация о продукте

Develop, manage, and deploy efficient machine learning applications with Python. Find the best deals on Building Data Science Applications with FastAPI at Ubuy Kyrgyzstan.
Publisher Packt Publishing
Publication date October 8, 2021
Language English
Print length 426 pages
ISBN-10 1801079218
ISBN-13 978-1801079211
Item Weight 1.6 pounds (730 grams)
Dimensions 7.5 x 0.96 x 9.25 inches (19.1 x 2.4 x 23.5 cm)

Who Should Buy?

Suitable For
  • Data Scientists

    Ideal for data scientists aiming to efficiently deploy machine learning models into web applications using FastAPI.

  • Software Developers

    Software developers looking to enhance their skills in building APIs and integrating machine learning solutions.

  • Machine Learning Engineers

    Perfect for ML engineers who need to manage and deploy robust data science applications in Python.

Not Suitable For
  • Beginners

    Not suitable for beginners without prior experience in Python or machine learning concepts.

ОПИСАНИЕ ТОВАРА

Building Data Science Applications with FastAPI: Develop, manage, and deploy efficient machine learning applications with Python

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Вопросы и ответы клиентов

  • вопрос: What topics are covered in 'Building Data Science Applications with FastAPI'?

    отвечать: This book covers essential topics such as developing RESTful APIs with FastAPI, implementing machine learning models, managing dependencies, and deploying applications efficiently. It dives into the intricacies of data handling, model management, and best practices for building scalable applications. For instance, readers will learn how to integrate machine learning workflows into web applications, making it suitable for both beginners and advanced users aiming to harness FastAPI’s capabilities.
  • вопрос: Who is the target audience for this book?

    отвечать: The target audience includes data scientists, machine learning engineers, and Python developers looking to enhance their skills in building web applications using FastAPI. It is particularly beneficial for those who have a foundational understanding of Python and are eager to implement data science projects in real-world settings. This book serves as a practical guide, helping to bridge the gap between theoretical knowledge and hands-on application development.
  • вопрос: What is FastAPI and why is it recommended for machine learning applications?

    отвечать: FastAPI is a modern web framework for building APIs with Python that is known for its high performance and intuitive design. It is particularly recommended for machine learning applications due to its support for asynchronous programming, automatic generation of OpenAPI documentation, and easy integration with data validation libraries like Pydantic. These features make it ideal for developing data-intensive applications where efficiency and speed are paramount.
  • вопрос: Can I learn about deployment practices for machine learning applications in this book?

    отвечать: Yes, the book includes practical guidance on deploying machine learning applications with FastAPI. It covers various deployment options such as using cloud services, containerization with Docker, and CI/CD practices. This content is crucial for those looking to take prototypes to production, ensuring that applications are not only developed, but also successfully managed and deployed for real-world use.
  • вопрос: Are there any hands-on examples included in the book?

    отвечать: Absolutely! The book is replete with hands-on examples that allow readers to follow along as they build their own data science applications. These examples include step-by-step instructions on implementing machine learning algorithms and deploying them with FastAPI. This practical approach solidifies learning by enabling readers to apply concepts in real scenarios, such as creating a predictive application for a dataset of their choosing.
  • вопрос: Does the book discuss error handling and logging in FastAPI applications?

    отвечать: Yes, the book addresses error handling and logging, which are critical for building robust applications. It explains how to implement effective error-handling strategies and use logging to monitor application behavior. This knowledge helps ensure that applications run smoothly and allows developers to troubleshoot issues effectively, ultimately leading to improved user experiences in deployed applications.
  • вопрос: Is prior knowledge of machine learning required to understand the content?

    отвечать: While some basic familiarity with machine learning concepts can be helpful, the book is structured to cater to readers with varying levels of expertise. It introduces necessary machine learning principles where relevant, ensuring that newcomers to the field can still grasp the application development process. This makes it a valuable resource for both aspiring data scientists and seasoned developers expanding their skill set.
  • вопрос: What programming languages are used in the examples throughout the book?

    отвечать: The primary programming language used in the examples is Python, as it is the backbone of data science and machine learning applications. FastAPI, being a Python framework, allows readers to utilize familiar syntax and libraries. The book encourages readers to leverage Python's rich ecosystem which includes libraries like NumPy, Pandas, and Scikit-learn, enabling them to easily manage data and train machine learning models.
  • вопрос: How does 'Building Data Science Applications with FastAPI' position itself against competitors?

    отвечать: This book positions itself uniquely by focusing on practical applications of FastAPI in the context of data science. Unlike many general programming books, it directly addresses the intersection of web development and machine learning. This practical approach combined with its performance-oriented focus makes it especially appealing to developers eager to learn how to efficiently deploy data science solutions.
  • вопрос: Where can I buy 'Building Data Science Applications with FastAPI' in Kyrgyzstan?

    отвечать: You can purchase 'Building Data Science Applications with FastAPI' from Ubuy in Kyrgyzstan. Ubuy offers a user-friendly online shopping experience, making it easy to find and acquire this essential resource for building data science applications with FastAPI. Their platform is designed to cater to various customer needs, ensuring you have access to the book quickly and efficiently.

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KGS 6910

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Особенности и преимущества

  • Develop efficient RESTful APIs for data science with modern Python
  • Cover the concepts of the FastAPI framework
  • Implement a FastAPI dependency to efficiently run a machine learning model
  • Integrate common Python data science libraries in a web backend
  • Deploy a performant and reliable web backend for a data science application

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