Learning Api Styles
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Learning API Styles
Author: Lukasz Dynowski
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2025-07-11
An application programming interface (API) enables data exchange in systems such as web applications, microservices, and IoT devices. In this hands-on book, authors Lukasz Dynowski and Marcin Dulak show software developers and architects how to design and implement REST, GraphQL, gRPC, webhooks, WebSocket, messaging APIs, and more. This book looks at the most popular API styles from a network, application, and architecture perspective. You'll learn how to determine the appropriate type of API for your application use case and how to tackle design decisions along the way. You'll also learn the trade-offs between various APIs and acquire practical knowledge of how to implement them. Explore the origins and evolution of API styles Learn network protocols that various APIs use Understand the trade-offs of each API style Select an appropriate API style Learn how to implement, secure, and document the APIs
TensorFlow 2 Pocket Reference
This easy-to-use reference for TensorFlow 2 design patterns in Python will help you make informed decisions for various use cases. Author KC Tung addresses common topics and tasks in enterprise data science and machine learning practices rather than focusing on TensorFlow itself. When and why would you feed training data as using NumPy or a streaming dataset? How would you set up cross-validations in the training process? How do you leverage a pretrained model using transfer learning? How do you perform hyperparameter tuning? Pick up this pocket reference and reduce the time you spend searching through options for your TensorFlow use cases. Understand best practices in TensorFlow model patterns and ML workflows Use code snippets as templates in building TensorFlow models and workflows Save development time by integrating prebuilt models in TensorFlow Hub Make informed design choices about data ingestion, training paradigms, model saving, and inferencing Address common scenarios such as model design style, data ingestion workflow, model training, and tuning
Hands-On APIs for AI and Data Science
To succeed in AI and data science, you must first master APIs. API skills are essential for AI and data science success. With this practical book, data scientists and software developers will gain hands-on experience developing and using APIs with the Python programming language and popular frameworks like FastAPI and StreamLit. Part 1 takes you step-by-step through coding projects to build APIs using Python and FastAPI and deploy them in the cloud. Part 2 teaches you to consume APIs in a data science project using industry-standard tools. And in Part 3, you'll use ChatGPT, the LangChain framework, and other tools to access your APIs with generative AI and large language models (LLMs). As you complete the chapters in the book, you'll be creating a professional online portfolio demonstrating your new skill with APIs, AI, and data science. You'll learn how to: Design APIs that data scientists and AIs love Develop APIs using Python and FastAPI Deploy APIs using multiple cloud providers Create data science projects such as visualizations and models using APIs as a data source Access APIs using generative AI and LLMs Author Ryan Day is a data scientist in the financial services industry and an open source developer.