Which orchestration tools does this skill support?

It includes templates and best practices for popular tools like Apache Airflow, Kubeflow, Dagster, and Prefect.

Can I use it for cloud-specific ML services?

Yes, it covers integration points for major cloud platforms including AWS SageMaker, Google Vertex AI, and Azure ML.

What is the ML Pipeline Workflow skill used for?

It provides structural guidance and code patterns for building complete machine learning pipelines, from data ingestion to production model monitoring.

How does it improve model reliability?

By implementing automated validation checkpoints, performance regression detection, and canary deployment strategies to ensure only high-quality models reach production.

ML Pipeline Workflow Orchestrator

Name: ML Pipeline Workflow Orchestrator
Author: Microck

byMicrock

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데이터 과학 및 ML

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

소개

This skill provides comprehensive architectural guidance and implementation patterns for building production-grade MLOps pipelines. It covers the entire machine learning lifecycle, including data ingestion, feature engineering, model training, validation, and automated deployment strategies. By leveraging industry-standard tools like Airflow, Kubeflow, and MLflow, it helps developers create reproducible, scalable, and observable ML workflows that ensure model reliability and performance in production environments.

주요 기능

Automated data validation and feature engineering pipeline templates
81 GitHub stars
Model validation frameworks for regression detection and A/B testing
Experiment tracking and hyperparameter management integration
End-to-end DAG orchestration patterns for Airflow, Dagster, and Kubeflow
Production deployment strategies including Canary and Blue-Green rollouts

사용 사례

Building reproducible ML training pipelines from scratch
Implementing automated retraining systems triggered by data drift
Transitioning experimental notebooks into production-ready MLOps workflows

소개

주요 기능

Automated data validation and feature engineering pipeline templates
81 GitHub stars
Model validation frameworks for regression detection and A/B testing
Experiment tracking and hyperparameter management integration
End-to-end DAG orchestration patterns for Airflow, Dagster, and Kubeflow
Production deployment strategies including Canary and Blue-Green rollouts

사용 사례

Building reproducible ML training pipelines from scratch
Implementing automated retraining systems triggered by data drift
Transitioning experimental notebooks into production-ready MLOps workflows