The API Development and Model Serving course is designed for developers, engineers, and technical product builders who are new to Generative AI but already have intermediate machine learning knowledge, basic Python proficiency, and familiarity with development environments such as VS Code, and who want to engineer, customize, and deploy open generative AI solutions while avoiding vendor lock-in.

The course teaches learners how to deploy and expose generative AI models through robust and scalable APIs. Beginning with FastAPI, learners design and implement REST endpoints for model inference, focusing on schema design, authentication, rate limiting, and error handling. The course then introduces the Model Context Protocol (MCP), comparing it with traditional API approaches and demonstrating how function calling and tool integration can extend model capabilities. In the final module, learners address scaling and performance, applying containerization with Docker, asynchronous request handling, load balancing, and monitoring techniques. Practical exercises also cover tunneling and remote access using ngrok for rapid prototyping. By the end, learners will have built a production-ready API with clear documentation and the ability to support both REST and MCP-inspired integration patterns, equipping them with the tools to serve generative AI applications efficiently and reliably.