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DeepSeek in Practice
DeepSeek in Practice

DeepSeek in Practice: From basics to fine-tuning, distillation, agent design, and prompt engineering of open source LLM

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Profile Icon Andy Peng Profile Icon Alex Strick van Linschoten Profile Icon Duarte O.Carmo
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$49.99
Paperback Nov 2025 478 pages 1st Edition
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Arrow left icon
Profile Icon Andy Peng Profile Icon Alex Strick van Linschoten Profile Icon Duarte O.Carmo
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$49.99
Paperback Nov 2025 478 pages 1st Edition
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$35.99 $39.99
Paperback
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Free Trial
Renews at $19.99p/m
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DeepSeek in Practice

What Is DeepSeek?

Artificial intelligence (AI) is rapidly evolving, and with it comes a suite of tools that allow developers, researchers, and innovators to build smarter, more adaptive systems. One such emerging tool is DeepSeek: a powerful, open-source large language model (LLM) designed to rival the capabilities of major LLMs such as GPT-4 and LLaMA. But what exactly is DeepSeek, and why should you care?

In this chapter, we’re going to dive into what DeepSeek is, how it fits into the broader AI landscape, and why it’s generating interest across the tech industry. You’ll gain an understanding of DeepSeek’s unique features and how it compares to other models in terms of training data, efficiency, and performance benchmarks.

By the end of this chapter, you’ll be equipped to understand the development of DeepSeek and key contributors to its success.

In this chapter, we’re going to cover the following main topics:

  • Introducing...

Introducing DeepSeek

DeepSeek is an open-source language model that aims to democratize and make advanced AI accessible. The first version, DeepSeek-R1, appeared on 20 January 2025, just before the Chinese New Year. Instead of shipping only a closed binary, the team published the weights, training scripts, and inference code, so anyone can examine or rebuild the system.

Released under the MIT license, the model carries no usage fees or strict terms. Anyone may run it locally or adapt it for new tasks. This freedom drew developers, researchers, teachers, and small firms worldwide. They apply DeepSeek-R1 in support bots, classroom aids, lab studies, and writing tools.

Benchmarks show DeepSeek-R1, competing with OpenAI-o3 and Gemini-2.5-Pro. It handles math, code, many languages, and complex prompts. The results suggest strong models need not be closed and underscores China’s growing role in frontier AI. The release also revived debates on open access and safety, and improving...

Understanding the technical breakthroughs of DeepSeek

What truly sets DeepSeek-R1 apart are the technical innovations embedded in its architecture and training process. These innovations enabled it to outperform many contemporary models and helped redefine how future LLMs might be built.

The training process

Before we begin with DeepSeek’s training process, let’s first take a look at the development process of leading LLMs, which usually consists of the following:

  1. Pretraining on massive corpora using self-supervised learning: In this stage, models are exposed to large-scale, diverse datasets such as books, websites, and code. The goal is to learn general language patterns without explicit labels. Common pretraining strategies include the following:
    1. Autoregressive modeling (e.g., GPT): The model predicts the next word in a sequence.
    2. Masked language modeling (e.g., BERT): The model predicts missing (masked) words.
    3. Permutation...

Versions and evolution of DeepSeek

The story of DeepSeek is not just about a single model launch. It’s about a continuously evolving ecosystem. Each iteration of DeepSeek introduces significant upgrades in reasoning, usability, safety, and integration across platforms. Understanding the evolution of DeepSeek is critical to appreciating its long-term vision and potential.

DeepSeek’s evolving ecosystem

Since its debut, DeepSeek has made significant strides across multiple domains, including language understanding, mathematical reasoning, code generation, and multimodal capabilities. Each version introduces meaningful enhancements, underscoring DeepSeek’s goal of democratizing high-performance language models without compromising on quality.

Understanding DeepSeek’s evolution provides critical insight into its growth trajectory, vision, and how it continues to disrupt both proprietary and open AI ecosystems. In the following table, we chronologically...

DeepSeek’s impact on the global AI ecosystem

The release of DeepSeek-R1 wasn’t just a technical milestone; it was a strategic turning point for the global AI industry. Its unique combination of open source accessibility and top-tier performance sent shockwaves through research labs, start-ups, and policy circles alike. The implications have spanned economic competition, academic acceleration, ethical discourse, and geopolitical realignment.

Market disruption and price wars

Before DeepSeek-R1, frontier-level LLMs were typically expensive, restricted to API-only access, or bound by licenses that limited commercial use. The release of DeepSeek-R1, with freely available model weights, inference code, and permissive licensing, marked a significant shift, making high-performing language models far more accessible.

DeepSeek-R1 made a notable impact on the AI landscape by introducing free, full access to a high-performing reasoning model. It demonstrated competitive...

Summary

In this chapter, we explored the foundations of DeepSeek, a powerful open source language model series that is shaping the next wave of AI innovation. We began by understanding what DeepSeek is and why it stands out in the growing field of LLMs. We took a closer look at the technical breakthroughs that power DeepSeek, from its training methodology and architecture to its impressive performance on key benchmarks. We also examined the broader impact DeepSeek is having on the global AI ecosystem, especially in terms of open source access, innovation democratization, and regional development. Finally, the last section provided an overview of the different versions of DeepSeek, illustrating its rapid evolution and ongoing improvements in capabilities. You should now be able to clearly articulate what DeepSeek is, what makes it technically significant, how it’s influencing the global AI landscape, and how its different versions reflect strategic design choices over time....

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Key benefits

  • 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
  • Purchase of the print or Kindle book includes a free PDF eBook

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.

Who is this book 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.

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
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Publication date : Nov 21, 2025
Length: 478 pages
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Language : English
ISBN-13 : 9781806020850
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Table of Contents

15 Chapters
Understanding and Exploring DeepSeek Chevron down icon Chevron up icon
What Is DeepSeek? Chevron down icon Chevron up icon
Deep Dive into DeepSeek Chevron down icon Chevron up icon
Prompting DeepSeek Chevron down icon Chevron up icon
Part 2: Using DeepSeek Chevron down icon Chevron up icon
Using DeepSeek: Case Studies Chevron down icon Chevron up icon
Building with DeepSeek Chevron down icon Chevron up icon
Agents with DeepSeek Chevron down icon Chevron up icon
Part 3: Distilling and Deploying DeepSeek Chevron down icon Chevron up icon
DeepSeek-Driven Fine-Tuning of Gemma 3 for Legal Reasoning Chevron down icon Chevron up icon
Deploying DeepSeek Models Chevron down icon Chevron up icon
Epilogue Chevron down icon Chevron up icon
Unlock Your Exclusive Benefits Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
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