ML Pipeline Workflow

About

This skill provides comprehensive guidance for architecting and implementing robust MLOps workflows. It bridges the gap between raw data and production-ready models by offering standardized patterns for data ingestion, feature engineering, distributed training, and automated deployment. Whether you are using Airflow for DAG orchestration or MLflow for experiment tracking, this skill ensures your ML lifecycle is reproducible, observable, and scalable, helping teams move from experimental notebooks to reliable production systems.

Key Features

• Comprehensive model validation and performance regression detection
• Production-grade deployment strategies including canary and blue-green releases
• Seamless integration with MLOps tools like Airflow, MLflow, and SageMaker
• Automated data validation and feature engineering pipelines
• End-to-end DAG orchestration for complex ML lifecycles
• 0 GitHub stars

Use Cases

• Setting up CI/CD workflows for machine learning artifacts and serving
• Implementing standardized data versioning and lineage tracking
• Building automated training and retraining pipelines for production models