Harold roop (7 Ergebnisse)

Taschenbuch. Zustand: Neu. Neuware.

Taschenbuch. Zustand: Neu. Neuware.

Taschenbuch. Zustand: Neu. Neuware - AI Agent Projects with Python: Build Practical AI Assistants, Research Agents, Automation Tools, Multi-Agent Systems and Intelligent ApplicationsYou have learned some Python. You have tried AI APIs. Maybe you have even built a chatbot. But when it is time to build an AI agent that can actually use tools, remember information, research a topic, or complete a task, everything suddenly feels more complicated.Which tools should you use How do you connect an AI model to Python functions How does an agent know what action to take next How do you add memory, RAG, automation, or multiple agents without getting lost in confusing documentation and scattered tutorials AI Agent Projects with Python gives you a practical way forward. Instead of filling pages with theory, this book teaches AI agent development by helping you build working projects step by step. You start with the basic structure of an agent and gradually move toward more capable intelligent applications.Inside, you will learn how to build a Python AI assistant, connect language models to tools, manage conversation history, create structured responses, and handle common errors. You will work with Retrieval-Augmented Generation (RAG) to connect agents to external knowledge and add memory so they can preserve useful information across interactions.As your skills grow, you will build research agents, automation agents, data analysis agents, and multi-agent systems. You will see how specialized agents can share work, manage information, and cooperate on larger tasks. You will also bring tools, retrieval, memory, application state, and user interaction together in a more complete intelligent application.The practical examples show you what the code is doing and why it works. You will also learn important development habits that can save you from costly mistakes, including input validation, permissions, execution limits, error handling, human approval, and safe API key management. The book does not stop when your agent starts working. You will learn how to test, evaluate, secure, monitor, and prepare AI agent applications for production. Topics such as logging, tracing, failures, retries, rate limits, cost management, and reliability help you understand what it takes to move beyond a small experiment.You do not need to be an AI researcher. Basic Python knowledge is enough to get started, and important concepts are explained as they appear. The goal is not to make you copy large blocks of code without understanding them. It is to help you develop practical skills you can reuse in your own AI projects. AI agents are quickly becoming an important part of modern software development. Do not spend months jumping between tutorials and trying to piece everything together on your own.Get your copy of AI Agent Projects with Python today and start building practical AI agents one project at a time.…

Taschenbuch. Zustand: Neu. Neuware.

Taschenbuch. Zustand: Neu. Neuware.

Taschenbuch. Zustand: Neu. Neuware - Mastering AI Observability: A Practical Guide to Monitoring, Evaluating, Debugging and Optimizing Production AI SystemsHave you ever deployed an AI application that looked perfect in testing, only to fail, slow down, hallucinate, or become expensive once real users started using it If you've spent hours digging through logs, searching online for answers, or trying to figure out why your AI system isn't behaving the way it should, you're not alone.Modern AI systems are powerful, but they can also be difficult to understand. A small change in a prompt, model, dataset, or retrieval pipeline can lead to inaccurate answers, higher costs, poor performance, or frustrated users. Most documentation explains how to build AI applications, but very little shows you how to keep them running reliably in production.Mastering AI Observability was written to solve that problem. This practical guide shows you how to monitor, evaluate, debug, and optimize production AI systems with confidence. Whether you're working with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, or modern machine learning applications, you'll learn how to understand what's happening inside your AI systems and fix problems before they affect your users.Inside this book, you'll learn how to: - Build observable AI systems from the ground up- Collect and analyze logs, metrics, traces, and telemetry- Monitor LLMs, AI agents, and RAG applications in production- Detect hallucinations, model drift, latency issues, and performance bottlenecks- Use OpenTelemetry, OpenInference, and modern observability tools- Debug AI failures and perform root cause analysis- Track token usage, infrastructure health, and operational costs- Improve reliability, scalability, security, and user experienceYou'll also find step-by-step instructions, practical examples, real-world case studies, complete code samples, diagrams, dashboards, checklists, and best practices that make even complex observability concepts easy to understand and apply.Drawing on proven engineering practices and real production scenarios, the author explains each concept in clear, simple language that helps you move from theory to real-world implementation without feeling overwhelmed. Don't wait until hidden AI failures damage your application, increase your costs, or erode user trust. Learn how to monitor your AI systems the right way from the very beginning.Get your copy of Mastering AI Observability today and start building AI applications that are reliable, transparent, efficient and ready for production.…

Taschenbuch. Zustand: Neu. Neuware.