Can I use this for real-time feature engineering?

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

How does it handle experiment tracking?

It offers integration strategies for tools like MLflow and Weights & Biases to manage metrics, hyperparameters, and model versions.

What orchestration tools does this skill support?

It provides patterns and guidance for popular tools like Apache Airflow, Dagster, Kubeflow Pipelines, and Prefect.

Does it include model monitoring?

Absolutely. It covers model performance drift monitoring, latency tracking, and automated validation before deployment.

ML Pipeline Workflow

Name: ML Pipeline Workflow
Author: Activer007

byActiver007

Ciencia de Datos y ML

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

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This skill provides comprehensive architectural guidance and implementation patterns for creating production-ready MLOps pipelines. It helps users design robust DAG-based workflows for data ingestion, feature engineering, and automated model training while integrating essential practices like experiment tracking, model versioning, and performance monitoring. Whether you are setting up a new pipeline using Airflow or Kubeflow or optimizing existing deployment strategies like blue-green or canary releases, this skill offers the templates and best practices needed for scalable machine learning operations.

Características Principales

End-to-end DAG orchestration for ML workflows
Continuous training and model performance monitoring
Experiment tracking and model registry integration
0 GitHub stars
Production-grade deployment strategies (Canary, Blue-Green)
Automated data validation and feature engineering pipelines

Casos de Uso

Designing a reproducible ML training pipeline from scratch
Automating model deployment and validation within a CI/CD environment
Implementing data drift detection and continuous retraining logic

Acerca de

Características Principales

End-to-end DAG orchestration for ML workflows
Continuous training and model performance monitoring
Experiment tracking and model registry integration
0 GitHub stars
Production-grade deployment strategies (Canary, Blue-Green)
Automated data validation and feature engineering pipelines

Casos de Uso

Designing a reproducible ML training pipeline from scratch
Automating model deployment and validation within a CI/CD environment
Implementing data drift detection and continuous retraining logic