MLflow MCP Server
by iRahulPandey
The MLflow MCP Server provides a natural language interface to MLflow via the Model Context Protocol (MCP). It allows querying your MLflow tracking server using plain English, simplifying the management and exploration of machine learning experiments and models.
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What is MLflow MCP Server?
The MLflow MCP Server is a system that connects to your MLflow tracking server and exposes MLflow functionality through the Model Context Protocol (MCP). It includes a client that provides a natural language interface to interact with the server using a conversational AI assistant.
How to use MLflow MCP Server?
First, start the MLflow MCP server using python mlflow_server.py. Then, make natural language queries using the client with python mlflow_client.py "Your Query". Ensure you have set the necessary environment variables like OPENAI_API_KEY and MLFLOW_TRACKING_URI.
Key features of MLflow MCP Server
Natural Language Queries
Model Registry Exploration
Experiment Tracking
System Information
Use cases of MLflow MCP Server
Exploring registered models using natural language
Listing and exploring MLflow experiments and runs
Getting details about specific models and experiments
Checking the status and metadata of your MLflow environment
FAQ from MLflow MCP Server
What is the default MLflow tracking URI?
What is the default MLflow tracking URI?
The default MLflow tracking URI is http://localhost:8080.
What OpenAI model is used by default?
What OpenAI model is used by default?
The default OpenAI model is gpt-3.5-turbo-0125.
What is the purpose of the MLflow MCP Server?
What is the purpose of the MLflow MCP Server?
The MLflow MCP Server provides a natural language interface to interact with MLflow.
What are the prerequisites for using this server?
What are the prerequisites for using this server?
The prerequisites include Python 3.8+, a running MLflow server, and an OpenAI API key.
Can I use this server for MLflow model predictions?
Can I use this server for MLflow model predictions?
Currently, the server does not support MLflow model predictions, but this is planned for future improvements.