This skill provides a comprehensive framework for architecting and implementing production-grade MLOps workflows. It guides users through the entire machine learning lifecycle, from data ingestion and feature engineering to experiment tracking and automated deployment strategies like canary or blue-green releases. By incorporating best practices for modularity, idempotency, and observability, it helps developers create reproducible, scalable, and monitorable ML systems using industry-standard tools like Airflow, Kubeflow, and MLflow.
Características Principales
01Model validation frameworks with performance regression detection
020 GitHub stars
03Automated data validation and versioned feature engineering
04End-to-end DAG orchestration for complex ML workflows
05Experiment tracking and model registry integration
06Advanced deployment patterns including canary and A/B testing
