Community Plugin

machine-learning-ops

ML model training pipelines, hyperparameter tuning, model deployment automation, experiment tracking, and MLOps workflows

1.2.1

Updated 2 months ago

AI Insight

Automates end-to-end machine learning workflows including model training, hyperparameter tuning, deployment, and experiment tracking for MLOps.

Why this plugin

Provides integrated, production-ready MLOps pipelines with specialized agents and skills, reducing manual setup and enabling efficient model lifecycle management within Claude's ecosystem.

Prerequisites

Requires Claude Code access and installation via marketplace command; dependencies like ML frameworks (TensorFlow/PyTorch) and tools (MLflow) are managed within the plugin.

machine-learning

automation

deployment

Install

Add the repository(one-time)

/plugin marketplace add engineerwithai/engineerwith-agents

Install the plugin

/plugin install machine-learning-ops@claude-code-workflows

Components

Commands

Machine Learning Pipeline - Multi-Agent MLOps Orchestration

/ml-pipeline

inherits

Design and implement a complete ML pipeline for: $ARGUMENTS

Agents

Expert data scientist for advanced analytics, machine learning, and statistical modeling. Handles complex data analysis, predictive modeling, and business intelligence. Use PROACTIVELY for data analysis tasks, ML modeling, statistical analysis, and data-driven insights.

Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring. Use PROACTIVELY for ML model deployment, inference optimization, or production ML infrastructure.

Build comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools. Implements automated training, deployment, and monitoring across cloud platforms. Use PROACTIVELY for ML infrastructure, experiment management, or pipeline automation.

Skills

ml-pipeline-workflow

inherits

Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.

Stats

Stars0

MaintenanceFair

Last Commit2 months ago

Community Promotion

Collections

Links

View on GitHub

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Plugin Marketplace JSON

Homepage

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