The Linear Regression MCP project demonstrates an end-to-end machine learning workflow using Claude and the Model Context Protocol (MCP), including data preprocessing, model training, and evaluation.

The Allora MCP Server is an implementation based on the Model Context Protocol (MCP) that provides the function of obtaining machine learning inference data from the Allora network, enabling AI systems to seamlessly access Allora prediction market data.

This project provides a natural language interaction interface for MLflow through the Model Context Protocol (MCP), allowing users to query and manage machine learning experiments and models in English. It includes server - side and client - side components.

This project provides a Model Context Protocol (MCP) service for MLflow through a natural language interface, simplifying the management and query of machine learning experiments and models.

This project demonstrates the training of a linear regression model using Claude and the Model Context Protocol (MCP) for an end-to-end machine learning workflow. Users only need to upload a CSV dataset, and the system can automatically complete the entire process of data preprocessing, model training, and evaluation (RMSE calculation).

The Cloudera ML Model Control Protocol (MCP) is a Python toolkit that provides functions for integrating with the Cloudera Machine Learning platform, including services such as file management, job scheduling, model management, and experiment tracking.

Statsource MCP Server is a Model Context Protocol server that provides statistical analysis and machine learning prediction functions, supporting access to multiple data sources and diverse statistical calculations.