Build
Creatednicknochnack
Provides a complete walkthrough for building a server to serve a trained Random Forest model and integrating it with Bee Framework for ReAct interactivity.
About
This repository offers a step-by-step guide to creating a server that serves a trained Random Forest model and integrates seamlessly with the Bee Framework for ReAct interactivity. It includes instructions on setting up the server, running the agent, and deploying a FastAPI-hosted ML server. The project leverages Python and provides links to reference materials for building MCP clients and the original ML server tutorial.
Key Features
- Deployment of a FastAPI-hosted ML server
- 16 GitHub stars
- Integration with Bee Framework for ReAct interactivity
- References to building MCP clients
- Step-by-step instructions for building an MCP server
- Example code for running the MCP server and agent
Use Cases
- Integrating machine learning models with ReAct-based applications
- Serving trained machine learning models through an MCP server
- Building interactive applications using Bee Framework
