Which orchestration tools does this skill support?

The skill provides architectural guidance for popular tools including Apache Airflow, Dagster, Kubeflow Pipelines, and Prefect.

What is the ML Pipeline Workflow skill?

It is a specialized capability for Claude Code that provides patterns, templates, and guidance for building complete MLOps pipelines from data ingestion to production deployment.

Can this skill help with model monitoring and drift?

Yes, it includes best practices for logging metrics at every stage and implementing monitoring to detect data drift and performance degradation.

Does it include templates for deployment?

Yes, it provides assets and reference documentation for various deployment patterns, including batch processing, real-time features, and continuous training.

ML Pipeline Workflow

Name: ML Pipeline Workflow
Author: Activer007

byActiver007

Data Science & ML

Orchestrates end-to-end MLOps pipelines from data preparation and model training to production deployment and monitoring.

About

This skill provides comprehensive guidance for building and automating production-grade machine learning pipelines, covering the entire lifecycle from data ingestion to deployment. It helps users design robust Directed Acyclic Graph (DAG) architectures, implement reproducible training workflows, and establish automated validation and deployment strategies using industry-standard tools like Airflow, Kubeflow, and MLflow. Whether you're setting up a batch training pipeline or continuous training triggered by data drift, this skill ensures modularity, versioning, and observability throughout your machine learning operations.

Key Features

Model training management with experiment tracking integration
End-to-end MLOps pipeline orchestration and DAG design patterns
Performance validation frameworks including A/B testing infrastructure
Automated data validation, feature engineering, and lineage tracking
Deployment automation using canary and blue-green strategies
0 GitHub stars

Use Cases

Designing scalable data-to-model pipelines using Airflow or Kubeflow
Building automated retraining workflows for production machine learning models
Implementing reproducible ML experiments with integrated monitoring and versioning

About

Key Features

Model training management with experiment tracking integration
End-to-end MLOps pipeline orchestration and DAG design patterns
Performance validation frameworks including A/B testing infrastructure
Automated data validation, feature engineering, and lineage tracking
Deployment automation using canary and blue-green strategies
0 GitHub stars

Use Cases

Designing scalable data-to-model pipelines using Airflow or Kubeflow
Building automated retraining workflows for production machine learning models
Implementing reproducible ML experiments with integrated monitoring and versioning