Applied DeepSeek: Building Intelligent Systems

Overview

Apply DeepSeek to real-world scenarios through detailed case studies and learn how to build robust AI-powered applications. Discover best practices for integrating DeepSeek into practical workflows and systems. This course guides learners through hands-on applications of DeepSeek, starting with benchmarking, prompt design, and response evaluation in diverse domains such as financial document extraction. Learners will explore the process of building AI-driven solutions, from technical requirements and context creation to deploying APIs and running models locally. The course also covers the integration of DeepSeek into agent workflows, enabling the development of intelligent agents and orchestrated systems. Learners engage with practical examples and step-by-step explanations, focusing on translating DeepSeek’s capabilities into effective solutions. The course emphasizes actionable insights and real-world integration strategies for developers and AI engineers. This course is part two of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.

Syllabus

Using DeepSeek: Case Studies

This module explores real-world applications of DeepSeek-R1 through industry case studies, focusing on benchmarking, prompt engineering, and performance evaluation. Learners will gain hands-on experience in designing prompts, analyzing extraction errors, and comparing DeepSeek-R1 with other document parsing tools. Best practices for hybrid approaches and strategies for financial document extraction are also covered.

Building with DeepSeek

This module guides learners through the practical implementation of DeepSeek models to solve real-world problems, including preparing health data, building and deploying APIs, and running inference both in the cloud and locally. Learners will gain hands-on experience with Docker, FastAPI, and various inference backends to operationalize AI solutions.

Agents with DeepSeek

This module introduces learners to building LLM-powered agents using DeepSeek, focusing on the integration of tools, workflows, and agentic applications. Through practical examples, such as summarizing scientific papers and orchestrator-worker patterns, learners will explore the challenges and solutions in developing effective AI agents.