ML Pipeline Workflow

소개

Streamline the entire machine learning lifecycle with comprehensive guidance on building scalable, reproducible, and automated MLOps pipelines. This skill provides end-to-end orchestration patterns for data ingestion, feature engineering, experiment tracking, and model serving, ensuring that ML models transition smoothly from development to production. Whether designing DAG-based workflows with tools like Airflow or implementing automated retraining and monitoring, it offers the architectural blueprints and best practices needed to maintain robust AI systems.

주요 기능

• Advanced deployment patterns including canary releases, blue-green strategies, and shadow deployments
• End-to-end DAG orchestration patterns for Airflow, Dagster, and Kubeflow
• Automated data validation, feature engineering, and versioning strategies
• Production monitoring frameworks for detecting performance regression and model drift
• 23,139 GitHub stars
• Comprehensive model training orchestration with experiment tracking and hyperparameter management

사용 사례

• Integrating experiment tracking and model registries into existing ML pipelines
• Implementing automated retraining and continuous deployment for production models
• Designing reproducible machine learning workflows from scratch