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automl-pipeline-setup

Automate end-to-end ML pipelines with H2O, Optuna, MLflow, and Airflow DAGs.

Updated Aug 27, 2026
Search Tags:#machine learning#feature engineering#data validation#pipeline#mlflow#automl#h2o
One-click install
npx skills add https://github.com/dengineproblem/agents-monorepo --skill automl-pipeline-setup-dengineproblem
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: automl-pipeline-setup
Source: https://github.com/dengineproblem/agents-monorepo/tree/main/.claude/skills/automl-pipeline-setup
Command: npx skills add https://github.com/dengineproblem/agents-monorepo --skill automl-pipeline-setup-dengineproblem

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires great_expectations, h2o, mlflow, optuna, airflow, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the setup and execution of automated machine learning (AutoML) pipelines, from data validation to model deployment.

Core Features & Use Cases

  • Automated Data Validation: Ensures data quality using Great Expectations.
  • Feature Engineering: Prepares data with scikit-learn and feature-engine.
  • AutoML Training: Leverages H2O.ai for efficient model training and hyperparameter tuning.
  • Experiment Tracking: Integrates with MLflow for logging and model registry.
  • Orchestration: Provides an Airflow DAG for end-to-end pipeline management.
  • Use Case: Automatically train and deploy a customer churn prediction model by providing a configuration file and data source.

Quick Start

Configure and run the AutoML pipeline for customer churn prediction using the provided YAML configuration.

Frequently Asked Questions about automl-pipeline-setup

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I set up an automated machine learning pipeline with Airflow and MLflow?▼

You can set up an automated machine learning pipeline by orchestrating the workflow using Airflow DAGs for data validation, feature engineering, and model training, while integrating MLflow for experiment tracking and model registry management.

What is the best way to automate data validation and feature engineering in an ML pipeline?▼

Automating data validation and feature engineering involves using Great Expectations to ensure data quality and applying scikit-learn with feature-engine to prepare data before training models in the pipeline.

Can I use H2O AutoML with Optuna for hyperparameter tuning in my pipeline?▼

Yes, you can use H2O AutoML for efficient model training and Optuna for hyperparameter tuning within the same pipeline to optimize model performance and automate the training process.

How do I train a customer churn prediction model using an AutoML pipeline?▼

To train a customer churn prediction model, configure the AutoML pipeline using a provided YAML configuration file and your data source to automatically execute data validation, training, and tracking.

Do I need Airflow to orchestrate my H2O AutoML and MLflow workflow?▼

Airflow is required to orchestrate the end-to-end machine learning workflow, providing reproducible and scalable ML operations by managing the execution of H2O training and MLflow tracking.

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