{ "item_title" : "Building AI APIs with Ray Serve", "item_author" : [" Jacob Arvidsson "], "item_description" : "A Hands-On Guide to FastAPI Integration, Real-Time Model Predictions, and Scalable Python Backend ServicesLearn how to turn machine learning models into practical, reliable AI-powered APIs using Python, Ray Serve, and FastAPI.Training a machine learning model is only part of building an AI application. The next challenge is making that model accessible through an API, handling prediction requests, and preparing the service to support real-world applications.Building AI APIs with Ray Serve provides a practical, beginner-friendly introduction to model serving and Python backend development. Through clear explanations, guided examples, and a continuous hands-on project, you'll learn how to connect trained models to web APIs, organize prediction workflows, and understand the foundations of scalable AI services.Rather than overwhelming you with advanced infrastructure concepts, this guide focuses on the essential skills needed to build, run, test, and improve your first AI prediction service.What You'll LearnRay Serve Fundamentals: Understand how Ray Serve works, why model serving matters, and how deployments handle prediction requests.FastAPI Integration: Connect Python web endpoints to machine learning models and create structured request-response workflows.Practical Model Deployment: Prepare and serve trained Scikit-learn models through accessible REST API endpoints.Real-Time Predictions: Accept incoming data, validate requests with Pydantic, and return consistent prediction results.Scalable Backend Architecture: Explore model replicas, request routing, and the foundations of distributed inference.API Testing and Debugging: Use Python testing tools to verify endpoint behavior, investigate errors, and improve reliability.Hands-On Application Development: Build a Customer Churn Prediction API that demonstrates how the components work together.A Practical Project From Start to FinishThroughout the book, you'll develop a Customer Churn Prediction API using Python, Scikit-learn, Ray Serve, FastAPI, Pydantic, Requests, and pytest.You'll progress from understanding the application architecture to implementing prediction endpoints, testing responses, and running a complete model-serving application.Each stage builds on the previous one, helping you develop practical skills without unnecessary complexity.Who This Book Is ForThis guide is designed for: Python developers exploring machine learning deployment.Beginners interested in AI backend engineering and model serving.Machine learning enthusiasts who want to make trained models accessible through APIs.Aspiring MLOps and AI infrastructure engineers building foundational skills.Software developers transitioning into practical AI application development.Basic Python knowledge is helpful, but previous experience with Ray Serve, FastAPI, or production model deployment is not required.Move beyond training models and learn how to make them useful in real applications.Building AI APIs with Ray Serve gives you a structured, hands-on starting point for developing Python-based prediction services and understanding the technologies behind scalable AI applications.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/9/79/816/739/9798167392793_b.jpg", "price_data" : { "retail_price" : "25.99", "online_price" : "25.99", "our_price" : "25.99", "club_price" : "25.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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