What orchestration tools does this skill support?

It provides comprehensive guidance and templates for industry-leading tools including Apache Airflow, Kubeflow, Dagster, and Prefect for DAG-based ML orchestration.

Does it include data versioning and quality checks?

Absolutely. The skill covers data validation using libraries like Great Expectations and TFX, as well as versioning strategies with tools like DVC.

Can this skill help with cloud-specific model deployment?

Yes, it includes integration patterns and serving architectures for major platforms such as AWS SageMaker, Google Vertex AI, and Azure ML.

How does this skill handle model experiment tracking?

It provides patterns for integrating experiment tracking and model registries using tools like MLflow and Weights & Biases to maintain full lineage and auditability.

MLOps Pipeline Workflow Orchestrator

Name: MLOps Pipeline Workflow Orchestrator
Author: Tahir-yamin

byTahir-yamin

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Ciencia de Datos y ML

Builds and manages comprehensive end-to-end MLOps pipelines from data preparation through production deployment and monitoring.

Acerca de

This skill facilitates the creation of robust, production-grade machine learning pipelines by providing standardized patterns for data ingestion, feature engineering, model training, and automated deployment. It bridges the gap between data science and DevOps by implementing industry-standard orchestration patterns, validation frameworks, and experiment tracking to ensure reproducible and scalable AI workflows across various cloud environments and tools like Airflow or Kubeflow.

Características Principales

Standardized deployment strategies for canary and blue-green releases
Model validation frameworks including A/B testing and regression detection
End-to-end DAG orchestration patterns for ML lifecycle management
3 GitHub stars
Seamless integration with experiment tracking tools like MLflow and W&B
Automated data validation and feature engineering pipeline templates

Casos de Uso

Implementing monitoring and performance drift detection for ML systems
Designing automated CI/CD pipelines for production machine learning models
Setting up reproducible training workflows with robust data versioning

Acerca de

Características Principales

Standardized deployment strategies for canary and blue-green releases
Model validation frameworks including A/B testing and regression detection
End-to-end DAG orchestration patterns for ML lifecycle management
3 GitHub stars
Seamless integration with experiment tracking tools like MLflow and W&B
Automated data validation and feature engineering pipeline templates

Casos de Uso

Implementing monitoring and performance drift detection for ML systems
Designing automated CI/CD pipelines for production machine learning models
Setting up reproducible training workflows with robust data versioning