"MLB" matching MCP servers:

Enables LLMs to manage and run machine learning training jobs on a remote server, including syncing code, submitting experiments, monitoring progress, reading TensorBoard metrics, and receiving completion notifications.

An implementation of the Model Context Protocol for the Opik platform that provides a unified interface for accessing Opik's capabilities, supporting multiple transport mechanisms for flexible integration with IDEs and other environments.

A server that integrates trained Random Forest models with Bee Framework, enabling ReAct interactivity for AI tools and agents.

Gitbub mcp

Execute Python code with persistent state across Claude conversations using a background Jupyter kernel. Supports creating notebooks, managing virtual environments, and maintaining variables/imports between code executions.

Enables routing of ML tasks like chat, sentiment analysis, recommendations, and summarization to appropriate models through a dynamic YAML-based registry. Provides async FastAPI endpoints with streaming support, retry logic, and pluggable model architecture for scalable ML inference.