What orchestration tools does this skill support?

The skill provides patterns and templates compatible with popular industry tools including Apache Airflow, Dagster, Kubeflow Pipelines, and Prefect.

Does it handle model versioning and tracking?

Absolutely. It integrates best practices for model registries like MLflow and Weights & Biases to ensure full lineage, versioning, and experiment tracking.

Can I use this for real-time feature engineering?

Yes, the skill includes documentation and patterns for both batch training pipelines and real-time feature processing architectures.

How does it help with model deployment?

It provides templates for various deployment patterns, including canary releases, blue-green deployments, and shadow deployments to ensure safe production rollouts.

ML Pipeline Workflow

Name: ML Pipeline Workflow
Author: carloscroc

bycarloscroc

データサイエンスとML

Builds and orchestrates end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment.

概要

This skill provides comprehensive guidance and templates for building production-grade Machine Learning pipelines. It covers the entire lifecycle including data ingestion, validation, feature engineering, experiment tracking, and automated deployment. Ideal for data scientists and MLOps engineers, it helps implement reproducible workflows using popular orchestration tools like Airflow or Kubeflow while ensuring best practices for model monitoring, versioning, and failure handling are maintained throughout the system.

主な機能

End-to-end DAG orchestration patterns for ML workflows
Deployment automation for canary and blue-green releases
0 GitHub stars
Automated data validation and quality check frameworks
Integrated model performance monitoring and drift detection
Experiment tracking and model registry integration templates

ユースケース

Automating data lineage and feature engineering processes
Building reproducible training workflows for production models
Implementing CI/CD pipelines for machine learning (MLOps)

概要

主な機能

End-to-end DAG orchestration patterns for ML workflows
Deployment automation for canary and blue-green releases
0 GitHub stars
Automated data validation and quality check frameworks
Integrated model performance monitoring and drift detection
Experiment tracking and model registry integration templates

ユースケース

Automating data lineage and feature engineering processes
Building reproducible training workflows for production models
Implementing CI/CD pipelines for machine learning (MLOps)