Production Development with DeepSeek

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.

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

Thirumalesh Konthala is a chief AI scientist, founder and director of DATAi2i, and head of India and partner at Alphanome.AI. With nearly 20 years of experience, he specializes in production AI, generative AI, and enterprise data science. Through DATAi2i, he advances agentic AI platforms and GenAI solutions for industrial automation and operational intelligence.

As advisor for AI product development and advanced DS strategy, he consults multiple organizations on building scalable AI architecture, implementing enterprise MLOps, and assembling high-performing data science and AI teams. He is passionately committed to bridging AI research and production-ready LLM deployment solutions.
His career spans Amazon, Cardlytics, DBS Bank, Novartis, and Franklin Templeton, where he built teams, pioneered the Purchase Graph deep learning architecture, and implemented enterprise MLOps on Kubernetes. Thirumalesh Konathala holds a PhD in AI, a masters in statistics from Central University of Hyderabad, and was trained at the Indian Statistical Institute, Kolkata.

Based in Vishakhapatnam, he actively mentors at universities and speaks at AI conferences on production AI and LLM deployments.