Which orchestration tools does this skill support?

The skill provides patterns and templates for industry-standard tools including Apache Airflow, Dagster, Kubeflow Pipelines, and Prefect.

Can I use this for data validation?

Absolutely. It includes best practices for data quality checks, versioning with tools like DVC, and maintaining data lineage throughout the pipeline.

Does it include model deployment strategies?

Yes, it covers advanced deployment patterns such as canary releases, blue-green deployments, and shadow mode to ensure safe production rollouts.

What is the ML Pipeline Workflow skill?

It is a specialized capability for Claude Code that provides domain-specific guidance, best practices, and implementation patterns for building end-to-end MLOps pipelines.

How does it handle experiment tracking?

The skill provides integration patterns for popular experiment tracking platforms like MLflow and Weights & Biases to manage metrics and model registries.

ML Pipeline Workflow

Name: ML Pipeline Workflow
Author: Activer007

byActiver007

데이터 과학 및 ML

Automates the end-to-end MLOps lifecycle from data preparation and model training to production deployment and monitoring.

소개

This skill provides a comprehensive framework for architecting and implementing production-grade machine learning pipelines. It guides users through the entire MLOps journey, including DAG-based orchestration, data validation, experiment tracking, and sophisticated deployment strategies like canary and blue-green releases. By using this skill, data scientists and ML engineers can ensure their workflows are reproducible, scalable, and integrated with industry-standard tools like Airflow, MLflow, and Kubeflow.

주요 기능

Experiment tracking and hyperparameter management
Performance regression detection and model validation
End-to-end MLOps orchestration and DAG design
0 GitHub stars
Automated data validation and feature engineering
Multi-pattern deployment (Canary, Blue-Green, Shadow)

사용 사례

Implementing data versioning and lineage for ML systems
Building reproducible ML training pipelines from scratch
Automating model deployment and production monitoring

소개

주요 기능

Experiment tracking and hyperparameter management
Performance regression detection and model validation
End-to-end MLOps orchestration and DAG design
0 GitHub stars
Automated data validation and feature engineering
Multi-pattern deployment (Canary, Blue-Green, Shadow)

사용 사례

Implementing data versioning and lineage for ML systems
Building reproducible ML training pipelines from scratch
Automating model deployment and production monitoring