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  • Book Overview & Buying DeepSeek in Practice
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DeepSeek in Practice

DeepSeek in Practice

By : Andy Peng, Alex Strick van Linschoten, Duarte O.Carmo
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DeepSeek in Practice

DeepSeek in Practice

1 (1)
By: Andy Peng, Alex Strick van Linschoten, Duarte O.Carmo

Overview of this book

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.
Table of Contents (17 chapters)
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12
Epilogue
14
Other Books You May Enjoy
15
Index

Summary

In this chapter, we took you from an initial idea to a fully working service that replaces boring smartwatch summaries with dynamic insights powered by DeepSeek. We began by showing how to quickly build a simple prototype using the DeepSeek API to meet our initial requirements. We then explored running models locally with distilled versions and built a lightweight CPU-based service that runs without GPUs.

Next, we focused on flexibility, demonstrating how libraries such as LiteLLM let you switch backend providers seamlessly without touching your application code. Finally, we scaled up by deploying LLMs on AWS using LMI containers and vLLM, so we can deploy larger models while staying in control.

By now, you have the toolkit to take any DeepSeek-powered application to production, whether you want to keep things small and efficient, run via an API, or deploy larger models in the cloud.

But many successful applications today are powered not by a single LLM, but by...

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DeepSeek in Practice
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