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.

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 Groundlight MCP server is a service for creating and managing image detectors. It supports multiple detection modes, including binary classification, multi-class classification, and counting functions, and provides interfaces for image queries and result retrieval.

A server project compatible with the MCP protocol, providing atomic-scale simulation functions through ASE, pymatgen, and machine learning interatomic potentials (MLIPs). It is currently under active development.

This is an MCP server that provides a standardized interface for Scikit-learn models, supporting functions such as model training, evaluation, data preprocessing, and persistence.

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.

YouTube MCP is an AI-based solution designed to enhance the YouTube content interaction experience through machine learning technology. It supports video search, subtitle access, and semantic search without using the official API.

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.

The OpenML MCP Server is a tool that provides access to the OpenML API through the MCP protocol, allowing users to directly query machine learning resources such as datasets, tasks, and processes from MCP-compatible clients (such as Claude Desktop).