ml-pipeline-workflow

What problem does it solve?

Building and managing robust, reproducible Machine Learning pipelines from data ingestion to model deployment is complex and critical for MLOps. This Skill provides comprehensive guidance for orchestrating the entire ML lifecycle, ensuring efficiency and reliability.

Core Features & Use Cases

• End-to-End Pipeline Design: Covers data preparation, model training, validation, and deployment stages.
• Orchestration Patterns: Guides on using tools like Airflow, Dagster, and Kubeflow for DAG-based workflows.
• Best Practices: Emphasizes modularity, idempotency, observability, and versioning for all pipeline components.
• Use Case: When you need to automate the retraining and deployment of a fraud detection model, this Skill helps you design a pipeline that handles data updates, model validation, and canary deployments.

Quick Start

Example: Define basic ML pipeline stages

This outlines the sequential steps of a typical ML pipeline.

stages = [ "data_ingestion", "data_validation", "feature_engineering", "model_training", "model_validation", "model_deployment" ]

Configure dependencies (see assets/pipeline-dag.yaml.template for full example)