Does it include support for cloud ML platforms?

Yes, it features integration points for major cloud providers including AWS SageMaker, Google Vertex AI, and Azure Machine Learning.

Can I use this for data preparation and feature engineering?

Absolutely. It includes specific modules for data validation, cleaning, feature transformation, and maintaining data lineage using tools like DVC or Great Expectations.

Which orchestration tools does this skill support?

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

Is this skill suitable for deep learning models?

Yes, it covers distributed training patterns and hyperparameter management suitable for complex deep learning architectures and large-scale datasets.

How does this skill handle model monitoring?

It provides guidance on setting up observability for model performance, detecting data drift, and implementing automated rollback mechanisms if performance degrades.

MLOps Pipeline Workflow

Name: MLOps Pipeline Workflow
Author: HermeticOrmus

byHermeticOrmus

Ciencia de Datos y ML

Orchestrates end-to-end machine learning pipelines from automated data preparation through model training, validation, and production deployment.

Acerca de

This skill provides a production-ready framework for managing the entire machine learning lifecycle, enabling developers to transition from manual scripts to robust, automated MLOps pipelines. It offers comprehensive guidance on workflow orchestration, data versioning, experiment tracking, and deployment strategies, ensuring that ML systems are reproducible, observable, and scalable using industry-standard tools like Airflow, MLflow, and Kubeflow.

Características Principales

End-to-end DAG orchestration patterns for complex ML lifecycles
Comprehensive model validation and performance regression detection
Automated data validation and feature engineering pipeline templates
0 GitHub stars
Integrated experiment tracking and model registry management
Advanced deployment strategies including Canary and Blue-Green rollouts

Casos de Uso

Automating model retraining and deployment based on data drift triggers
Implementing reproducible training pipelines with full data and model lineage
Scaling experimental ML code into production-grade automated workflows

Acerca de

Características Principales

End-to-end DAG orchestration patterns for complex ML lifecycles
Comprehensive model validation and performance regression detection
Automated data validation and feature engineering pipeline templates
0 GitHub stars
Integrated experiment tracking and model registry management
Advanced deployment strategies including Canary and Blue-Green rollouts

Casos de Uso

Automating model retraining and deployment based on data drift triggers
Implementing reproducible training pipelines with full data and model lineage
Scaling experimental ML code into production-grade automated workflows