Which orchestration tools does this skill support?

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

Does it help with experiment tracking?

Yes, it offers integration strategies for major platforms like MLflow and Weights & Biases to track metrics and manage model registries.

What deployment strategies are covered?

It includes implementation guidance for canary releases, blue-green deployments, shadow deployments, and automated rollback mechanisms.

Can I use this for real-time feature engineering?

Yes, it includes reference patterns for both batch training pipelines and real-time feature processing architectures.

ML Pipeline Workflow

Name: ML Pipeline Workflow
Author: amurata

byamurata

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Ciencia de Datos y ML

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

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This skill streamlines the creation of production-grade machine learning pipelines by providing structured guidance for every stage of the MLOps lifecycle, including data ingestion, validation, training, and automated deployment. It is particularly useful for engineers and data scientists looking to implement reproducible, DAG-based workflows using industry-standard tools like Airflow, Kubeflow, or MLflow, ensuring model reliability through rigorous validation and monitoring patterns.

Características Principales

End-to-end DAG orchestration for complex ML workflows
Automated data validation and feature engineering patterns
Standardized model training and hyperparameter management
Robust validation frameworks for A/B testing and regression detection
Multi-pattern deployment strategies including canary and blue-green
3 GitHub stars

Casos de Uso

Automating data-to-deployment workflows with CI/CD integration
Implementing model monitoring and drift detection systems
Building reproducible training pipelines from scratch

Acerca de

Características Principales

End-to-end DAG orchestration for complex ML workflows
Automated data validation and feature engineering patterns
Standardized model training and hyperparameter management
Robust validation frameworks for A/B testing and regression detection
Multi-pattern deployment strategies including canary and blue-green
3 GitHub stars

Casos de Uso

Automating data-to-deployment workflows with CI/CD integration
Implementing model monitoring and drift detection systems
Building reproducible training pipelines from scratch