What orchestration tools does this skill support?

It provides patterns and templates for major orchestration tools including Apache Airflow, Dagster, Kubeflow Pipelines, and Prefect.

Can I use this for real-time machine learning applications?

Yes, it includes architectural guidance for both batch training pipelines and real-time feature engineering workflows integrated with streaming data.

Does it include model performance monitoring?

Absolutely, the skill covers post-deployment monitoring for model drift, performance regression, and infrastructure latency to ensure production stability.

How does it handle data versioning and reproducibility?

The skill implements best practices for data lineage and versioning using tools like DVC, ensuring every model can be traced back to the exact dataset version used for training.

MLOps Pipeline Workflow

Name: MLOps Pipeline Workflow
Author: HermeticOrmus

byHermeticOrmus

数据科学与机器学习

Orchestrates end-to-end machine learning pipelines from data preparation and training to production deployment and monitoring.

关于

This skill provides a comprehensive framework for building and managing production-ready machine learning lifecycles. It guides users through creating modular, versioned, and observable pipelines using industry-standard orchestration tools like Airflow, Kubeflow, or Dagster. Whether you are automating reproducible training workflows, implementing data validation checks, or setting up canary deployments, this skill ensures best practices for data lineage, experiment tracking, and model serving are integrated into your ML infrastructure for maximum reliability.

主要功能

End-to-end DAG orchestration design for Airflow, Kubeflow, and Dagster
Model training orchestration with integrated experiment tracking
Automated data validation and feature engineering pipeline templates
Pre-deployment validation frameworks for performance and regression checks
0 GitHub stars
Advanced deployment automation including canary and blue-green strategies

使用场景

Automating continuous training workflows triggered by data drift detection
Building reproducible batch training pipelines for enterprise ML models
Implementing modular data processing pipelines with built-in quality checks

关于

主要功能

End-to-end DAG orchestration design for Airflow, Kubeflow, and Dagster
Model training orchestration with integrated experiment tracking
Automated data validation and feature engineering pipeline templates
Pre-deployment validation frameworks for performance and regression checks
0 GitHub stars
Advanced deployment automation including canary and blue-green strategies

使用场景

Automating continuous training workflows triggered by data drift detection
Building reproducible batch training pipelines for enterprise ML models
Implementing modular data processing pipelines with built-in quality checks