Model choice is now an architecture decision, not a default. DeepSeek gives teams open weight models they can call through a hosted API or run on their own infrastructure, and this Specialization covers building applications on either.
You start with API integration, prompt design, and a conversational developer assistant. You then make responses reliable through schemas, validation, tool calling, and retrieval, and finish with agents that plan, act under human approval, and run behind a deployed endpoint.
By the end of this Specialization, you will be able to:
• Integrate the DeepSeek API with secure key handling and resilient request logic.
• Design and version prompts for explanation, debugging, refactoring, and testing.
• Return validated structured output using JSON schemas and Pydantic models.
• Connect calculators, external APIs, and databases through function calling.
• Build retrieval augmented generation grounded in your own document collections.
• Deploy an evaluated, containerized agent behind a FastAPI service.
This Specialization suits software developers, AI engineers, backend engineers, and technical leads assessing open weight models for real workloads. It assumes working Python and comfort calling APIs, and no background in LLM application development, retrieval, or agents.
Enroll now to build DeepSeek applications you can evaluate, secure, and deploy.
Applied Learning Project
Across the Specialization, you complete hands-on projects that mirror the work of building an LLM application end to end. You configure a DeepSeek project, secure your keys, and build developer utilities that explain, debug, refactor, and test code, then assemble them into a conversational assistant with managed context. You enforce structured output with Pydantic, build a support ticket classifier, connect calculators, external APIs, and a restricted database through function calling, and construct a retrieval pipeline that answers from your own documents with citations. Finally, you will build a controlled agent with Model Context Protocol tool access and human approval, evaluate it against a dataset you create, test it for prompt injection, and deploy it through FastAPI in a Docker container.
