Production Development with DeepSeek Building and deploying scalable DeepSeek models with LoRA, QLoRA, and Docker

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.

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