Multimodal models like DeepSeek are redefining what modern systems can achieve. With its reinforcement learning driven architecture, DeepSeek represents a new shift in adaptability, efficiency, and real-world intelligence making it highly useful for today’s developers, engineers, and AI enthusiasts.

The book is structured to follow the production flow, beginning with core principles of DeepSeek, model types (language, vision, distilled), and the critical choice between cloud APIs and local LLMs. It takes you through architecture of DeepSeek in a clear, practical manner. Each chapter explores a specific aspect, understanding its core design, comparing it with traditional deep learning, optimizing and fine-tuning workflows, building multimodal applications, and deploying models seamlessly using Docker. You will then get hands-on with environment setup before diving into supervised fine-tuning (SFT) with LoRA/QLoRA and performance-boosting reinforcement learning (RL) using GRPO techniques. Along the way, you will learn through hands-on coding exercises, practical use cases, and best practices suited for production-grade AI.

By the end, along with understanding how DeepSeek works, you will also know how to make it work for you. You will gain the skills to build AI solutions, customize models for user needs, deploy scalable inference endpoints, and confidently integrate DeepSeek into real-world systems.

What you will learn

● Understand architecture of DeepSeek and RL foundations.

● Compare DeepSeek with conventional deep learning model approaches.

● Fine-tune DeepSeek effectively for specialized real-world production-grade tasks.

● Build multimodal applications using advanced capabilities of DeepSeek.

● Deploy DeepSeek models efficiently using Docker and containers.

● Integrate DeepSeek into automation, chatbots, and industry workflows.

● Apply best practices for scalable, production-ready AI solutions.

Who this book is for

This book is ideal for AI enthusiasts, ML engineers, data scientists, researchers, and developers who want to understand and apply RL-driven capabilities of DeepSeek. It is especially useful for professionals with basic deep learning and Python experience looking to build practical, production-ready AI systems.

Table of Contents

1. Introduction to DeepSeek

2. Understanding the Essentials of DeepSeek

3. Overview of DeepSeek Models and Types

4. Production Approaches

5. Setup and Environment

6. Supervised Fine-tuning

7. Reinforcement Learning from Human Feedback

8. Deploying DeepSeek with Inference and RAG

9. Deploying DeepSeek with Cloud, Multimodal and Agents

10. Dockerization and Real-world Applications