Can I use this for real-time feature engineering?

Yes, the skill includes guidance for both batch training pipelines and stream processing patterns for real-time feature engineering.

Does it help with model monitoring after deployment?

Absolutely. It covers monitoring strategies for model performance, data drift detection, latency, and throughput in production environments.

How does this skill handle model validation?

It implements validation frameworks for performance regression detection, A/B testing infrastructure, and provides pre-deployment checklists.

What orchestration tools does this skill support?

It provides architecture patterns and templates for industry-standard tools including Apache Airflow, Dagster, Kubeflow Pipelines, and Prefect.

ML Pipeline Workflow

Name: ML Pipeline Workflow
Author: amurata

byamurata

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Ciencia de Datos y ML

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

Acerca de

This skill provides comprehensive guidance for architecting and automating the full machine learning lifecycle, enabling developers to create robust MLOps workflows. It covers everything from data ingestion and feature engineering to experiment tracking, model serving, and continuous monitoring. Whether you are using Airflow, Kubeflow, or cloud-native platforms like SageMaker, this skill helps implement best practices for reproducibility, observability, and modular pipeline design to ensure production-grade ML systems.

Características Principales

Automated data validation and feature engineering pipeline patterns
Production deployment strategies including canary and blue-green releases
3 GitHub stars
Experiment tracking and model registry integration for reproducibility
End-to-end DAG orchestration for ML workflows and dependencies
Comprehensive model validation and performance regression detection

Casos de Uso

Automating the deployment of model updates using integrated MLOps practices
Designing a reproducible training pipeline for a new machine learning model
Setting up monitoring for data drift and model performance in production

Acerca de

Características Principales

Automated data validation and feature engineering pipeline patterns
Production deployment strategies including canary and blue-green releases
3 GitHub stars
Experiment tracking and model registry integration for reproducibility
End-to-end DAG orchestration for ML workflows and dependencies
Comprehensive model validation and performance regression detection

Casos de Uso

Automating the deployment of model updates using integrated MLOps practices
Designing a reproducible training pipeline for a new machine learning model
Setting up monitoring for data drift and model performance in production