Cloudera ML MCP

Example Use Cases

• 1

  Upload local directories (preserving structure) into a Cloudera ML project programmatically.

• 2

  Create, list, and delete CML jobs and view job runs for automating ML workflows.

• 3

  Manage models, deployments, experiments, and project files inside a Cloudera ML project.

Description

This MCP provides a Python integration for Cloudera Machine Learning (CML), offering functionality to upload folders, create and manage jobs, list project files, handle models and deployments, and track experiments and runs. It can run as a stdio-based MCP server (suitable for use with Claude Desktop) or be imported into other Python code. Configuration is handled via environment variables or an initialization config, and the project is distributed under an open-source MIT license. The tool is useful for automating common CML project and model lifecycle tasks programmatically.