machine-learning-ops — Claude Code plugin: features and install
MLOps workflows plugin for training pipelines, experiment tracking, hyperparameter tuning, and automated deployment in Claude Code, Codex, and Cursor.
/plugin marketplace add wshobson/agents
/plugin install machine-learning-ops@claude-code-workflowsWhat machine-learning-ops does
This plugin structures machine learning operations inside your coding environment. It focuses on ML training pipelines and hyperparameter tuning. You also get workflows for experiment tracking alongside automated model deployment.
The package supports Claude Code, Codex, and Cursor. Setting it up in Claude Code requires registering the wshobson/agents marketplace first; skipping that step will cause the install command to fail. Seth Hobson maintains the repository under the MIT license.
What it helps with
- build pipelines for model training
- automate machine learning model deployment
- track machine learning experiment results
- configure hyperparameter tuning runs
Example prompts for the agent
- Help me set up an automated model training pipeline with experiment tracking
- Create an automation workflow to deploy our newly trained model
- Structure a hyperparameter tuning routine for this machine learning project
How to install machine-learning-ops
Plugin marketplace
/plugin marketplace add wshobson/agents/plugin install machine-learning-ops@claude-code-workflowsStep by step, with commands for each platform: Claude Code plugins: marketplaces, installing, and building your own
Questions about machine-learning-ops
How do I install machine-learning-ops in Claude Code?
Run /plugin marketplace add wshobson/agents followed by /plugin install machine-learning-ops@claude-code-workflows.
What does the machine-learning-ops plugin do?
It provides workflows for model training pipelines, hyperparameter tuning, experiment tracking, and deployment automation.
Which platforms support machine-learning-ops?
The plugin works with Claude Code, Codex, and Cursor.
Source text
machine-learning-ops · MIT
ML model training pipelines, hyperparameter tuning, model deployment automation, experiment tracking, and MLOps workflows
© Seth Hobson · MIT · wshobson/agents
