Can I use this for real-time feature engineering?

Yes, it includes guidance for both batch training pipelines and real-time stream processing patterns for feature pipelines.

How does it handle model validation?

It implements automated validation frameworks that check for performance regressions, run A/B test infrastructure, and provide pre-deployment checklists.

Does it include cloud-specific deployment advice?

Yes, it covers integration points for AWS SageMaker, Google Vertex AI, Azure ML, and cloud-agnostic Kubernetes serving with KServe.

What orchestration tools does this skill support?

The skill provides patterns and templates for popular orchestration tools including Apache Airflow, Dagster, Kubeflow Pipelines, and Prefect.

ML Pipeline Workflow

Name: ML Pipeline Workflow
Author: as4584

byas4584

Data Science & ML

Orchestrates end-to-end MLOps pipelines from data preparation and model training to production deployment and monitoring.

About

This skill provides comprehensive guidance for building and managing production-grade Machine Learning pipelines. It covers the entire lifecycle of an ML project, including modular data preparation, scalable training job orchestration, rigorous model validation, and automated deployment strategies like canary or blue-green releases. By implementing DAG-based workflows and integrating with tools like Airflow, Kubeflow, or MLflow, it helps teams ensure reproducibility, observability, and reliability in their machine learning operations while reducing manual intervention.

Key Features

Modular data validation and feature engineering pipeline design
Production deployment strategies including canary and blue-green releases
Automated model validation frameworks and regression detection
0 GitHub stars
DAG-based workflow orchestration patterns (Airflow, Dagster, Kubeflow)
Experiment tracking and model registry integration with MLflow and W&B

Use Cases

Building reproducible ML training pipelines from scratch
Implementing MLOps best practices for production-grade AI systems
Automating model deployment and validation workflows

About

Key Features

Modular data validation and feature engineering pipeline design
Production deployment strategies including canary and blue-green releases
Automated model validation frameworks and regression detection
0 GitHub stars
DAG-based workflow orchestration patterns (Airflow, Dagster, Kubeflow)
Experiment tracking and model registry integration with MLflow and W&B

Use Cases

Building reproducible ML training pipelines from scratch
Implementing MLOps best practices for production-grade AI systems
Automating model deployment and validation workflows