API Development and Model Serving

Overview

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.

Syllabus

Building REST APIs with FastAPI

Learn how to build practical REST APIs that turn your models into usable services. You will create inference endpoints, design request and response schemas, and implement authentication, rate limiting, and error handling to keep your APIs secure and reliable. By the end, you will have hands on experience developing a FastAPI service that teammates and applications can call seamlessly, a core skill for production ML engineers.

Model Context Protocol (MCP) and Tool Integration

Explore how Model Context Protocol (MCP) enables models to connect directly with tools and systems. You’ll compare MCP with traditional APIs, implement function calling, and practice integrating MCP into FastAPI endpoints. These skills show you how to extend models beyond simple outputs, giving them the ability to take real actions—a capability increasingly expected in applied AI systems.

Scaling and Load Management

Learn how to prepare APIs for production by making them scalable and resilient. You’ll use Docker to containerize services, apply asynchronous request handling, and configure load balancing to support real workloads. You’ll also monitor performance and optimize bottlenecks, gaining the practical skills to ensure your model APIs stay reliable when demand grows.