ML model training pipelines, hyperparameter tuning, model deployment automation, experiment tracking, and MLOps workflows
Installation
npx claude-plugins install @amurata/claude-code-workflows/machine-learning-ops
Contents
Folders: agents, commands, skills
Included Skills
This plugin includes 1 skill definition:
ml-pipeline-workflow
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.
View skill definition
ML Pipeline Workflow
Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment.
Overview
This skill provides comprehensive guidance for building production ML pipelines that handle the full lifecycle: data ingestion → preparation → training → validation → deployment → monitoring.
When to Use This Skill
- Building new ML pipelines from scratch
- Designing workflow orchestration for ML systems
- Implementing data → model → deployment automation
- Setting up reproducible training workflows
- Creating DAG-based ML orchestration
- Integrating ML components into production systems
What This Skill Provides
Core Capabilities
Pipeline Architecture
- End-to-end workflow design
- DAG orchestration patterns (Airflow, Dagster, Kubeflow)
- Component dependencies and data flow
- Error handling and retry strategies
Data Preparation
- Data validation and quality checks
- Feature engineering pipelines
- Data versioning and lineage
- Train/validation/test splitting strategies
Model Training
- Training job orchestration
- Hyperparameter management
- Experiment tracking integration
- Distributed training patterns
Model Validation
- Validation frameworks and metrics
- A/B testing infrastructure
- Performance regression detection
- Model comparison workflows
Deployment Automation
- Model serving patterns
- Canary deployments
- Blue-green deploy