Building Agentic AI: Workflows, Fine-Tuning, Optimization, and Deployment

Description

Discover the future of AI with Building Agentic AI: Workflows, Fine-Tuning, Optimization, and Deployment. This groundbreaking book isn't just about teaching you the best ways to use AI and LLMs--it's about equipping you with the tools to understand and keep up with the ever-evolving world of artificial intelligence.

Author Sinan Ozdemir's goal is to help you grasp how AI systems work, what makes them tick, and how to extract valuable information from them. By the end of this journey, you'll not only master the best practices for using AI and LLMs today but also be prepared for the advancements of tomorrow.

The book is organized into three engaging "acts":

• In Act I, you'll dive into the basics of LLMs and AI, learning to build robust, production-ready workflows while focusing on Retrieval-Augmented Generation (RAG) workflows.
• Act II explores the world of AI Agents, covering the theoretical foundations of autonomous systems and case studies like multi-agent SDRs and Voice bots.
• Act III delves into advanced topics such as supervised fine-tuning, deployment considerations, and reinforcement learning.

Think of this as a recipe book, where you can pick and choose the case studies most relevant to you, ensuring you're always prepared for the next big thing in AI.

Sample Content

Table of Contents

Preface
Acknowledgments
About the Author

Part I: Getting Started with Foundations of AI, LLMs, and Experimentation
Chapter 1: An Introduction to AI, LLMs, and Agents
Chapter 2: First Steps with LLM Workflows
Chapter 3: AI Evaluation Plus Experimentation

Part II: Moving the Needle with AI Agents, Workflows, and Multimodality
Chapter 4: First Steps with AI Agents and Multi-Agent Workloads
Chapter 5: Enhancing Agents with Prompting, Workflows, and More Agents
Chapter 6: Moving Beyond Natural Language: Multimodal and Coding AI

Part III: Optimizing Workloads with Fine-Tuning, Frameworks, and Reasoning LLMs
Chapter 7: Reasoning LLMs and Computer Use
Chapter 8: Fine-Tuning AI for Calibrated Performance
Chapter 9: Optimizing AI Models for Production

Index

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