Gain hands-on experience building, fine-tuning, and deploying GenAI applications using DeepSeek

Key Features

• Explore DeepSeek’s architecture, training data, and reasoning capabilities
• Build agents, fine-tune with distillation, and deploy with CI/CD pipelines
• Apply DeepSeek to real-world use cases like coding, ideation, and legal analysis

Book Description

Learn how to build, fine-tune, and deploy AI systems using DeepSeek, one of the most influential open-source large language models available today. This book guides you through real-world DeepSeek applications—from understanding its core architecture and training foundations to developing reasoning agents and deploying production-ready systems.

Starting with a concise synthesis of DeepSeek's research, breakthroughs, and open-source philosophy, you’ll progress to hands-on projects including prompt engineering, workflow design, and rationale distillation. Through detailed case studies—ranging from document understanding to legal clause analysis—you’ll see how to use DeepSeek in high-value GenAI scenarios.

You’ll also learn to build sophisticated agent workflows and prepare data for fine-tuning. By the end of the book, you’ll have the skills to integrate DeepSeek into local deployments, cloud CI/CD pipelines, and custom LLMOps environments.

Written by experts with deep knowledge of open-source LLMs and deployment ecosystems, this book is your comprehensive guide to DeepSeek’s capabilities and implementation.

What you will learn

• Discover DeepSeek's unique traits in the LLM landscape
• Compare DeepSeek's multimodal features with leading models
• Consume DeepSeek via the official API, Ollama, and llama.cpp
• Use DeepSeek for coding, document understanding, and creative ideation
• Integrate DeepSeek with third-party platforms like OpenRouter and Cloudflare
• Distill and deploy DeepSeek models into production environments
• Identify when and where to use DeepSeek
• Understand DeepSeek's open philosophy

Who this book is for

AI engineers, developers, and builders working with open-source LLMs who want to integrate DeepSeek into GenAI applications, agent workflows, or deployment pipelines. Readers should have hands-on experience with Python, APIs, and tools like Ollama or llama.cpp, and a solid understanding of machine learning concepts.

Table of Contents

Part 1: Understanding and Exploring Deep Seek

Chapter 1: What Is DeepSeek?

Chapter 2: Deep Dive into DeepSeek

Chapter 3: Prompting DeepSeek

Part 2: Using DeepSeek

Chapter 4: Using DeepSeek: Case Studies

Chapter 5: Building with DeepSeek

Chapter 6: Agents with DeepSeek

Part 3: Distilling and Deploying DeepSeek

Chapter 7: DeepSeek-Driven Fine-Tuning of Gemma 3 for Legal Reasoning

Chapter 8: Deploying DeepSeek Models

Chapter 9: Epilogue

About the Authors

Andy Peng is a Senior Engineer at Amazon. He specializes in large language model inference optimization and evaluation for models like DeepSeek, Qwen, and Claude. His work spans AWS Bedrock, SageMaker, Amazon S3, AWS Fargate, AWS App Runner, Alexa Health & Wellness, and fintech. A NeurIPS 2025 Chair and program committee member for ICML, ICLR, KDD, and NeurIPS, he contributes to CNCF and the Linux Foundation, mentors at the University of Washington, and serves as a Resident Expert at the AI2 Incubator.

Alex Strick van Linschoten is a Machine Learning Engineer at ZenML. His work focuses on bridging the gap between machine learning research and production deployment, particularly within the LLMOps space. He leads and maintains the LLMOps Database, a comprehensive collection of over 1,000 case studies examining LLMOps and GenAI implementations in production environments. He transitioned to software engineering after earning a PhD in History and spending 15 years living and working as a historian and researcher in Afghanistan. He has authored, edited, and translated several books based on his historical research and is currently based in Delft, the Netherlands.

Duarte O.Carmo is a technologist from Lisbon, Portugal, now based in Copenhagen, Denmark. For the past decade, he's worked at the intersection of machine learning, artificial intelligence, software, data, and people. He has helped solve problems for both global corporations and small startups across industries such as healthcare, finance, agriculture, and advertising. His approach to solving tough problems always starts with the same thing: people. For the past five years, he's been running his one-man consulting company, working with clients of all sizes and across industries. He's also a regular speaker in the Python and machine learning communities and an active writer.