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ML Pipeline Templates

$49

End-to-end ML pipelines: data ingestion, preprocessing, training, evaluation, and deployment orchestration.

📁 8 files🏷 v1.0.0
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📁 File Structure 8 files

ml-pipeline-templates/ ├── LICENSE ├── README.md ├── config.example.yaml ├── docs/ │ ├── checklists/ │ │ └── pre-deployment.md │ ├── overview.md │ └── patterns/ │ └── pattern-01-train-evaluate-deploy.md └── templates/ └── config.yaml

📖 Documentation Preview README excerpt

ML Pipeline Templates

End-to-end ML pipeline templates covering the full lifecycle: data ingestion, preprocessing, feature engineering, model training, evaluation, and deployment. Built for reproducibility and automation.

What's Included

  • Data ingestion pipeline templates (batch and streaming)
  • Preprocessing and feature engineering stages
  • Training pipeline with hyperparameter configuration
  • Model evaluation and comparison stages
  • Deployment pipeline with validation gates
  • Orchestration configs for Airflow, Prefect, and Kubeflow
  • Pipeline testing and CI/CD integration

Quick Start


# 1. Copy the example config
cp config.example.yaml config.yaml

# 2. Install dependencies
pip install -r requirements.txt

# 3. Run the example pipeline locally
python -m pipelines.train_pipeline --config config.yaml

Prerequisites

  • Python 3.9+
  • Pipeline orchestrator (Airflow, Prefect, or Kubeflow)
  • Cloud storage for artifacts (S3/GCS)
  • Docker (for containerized pipeline steps)

Contents


ml-pipeline-templates/
  config.example.yaml
  docs/
    overview.md
    patterns/
      pattern-01-*.md
    checklists/
      pre-deployment.md
  templates/
    config.yaml

Support

For questions or issues, contact: megafolder122122@hotmail.com

License

MIT License - Copyright 2026 Jesse Mikkola. See LICENSE for details.

📄 Code Sample .yaml preview

config.example.yaml # ML Pipeline Templates - Example Configuration # Copy this file to config.yaml and update values for your environment pipeline: name: "training-pipeline" orchestrator: "prefect" # airflow, prefect, kubeflow schedule: null # "0 0 * * *" for daily data: source: type: "file" # file, database, api, s3 path: "./data/raw/" preprocessing: steps: - "remove_nulls" - "normalize_features" - "encode_categoricals" split: train: 0.7 validation: 0.15 test: 0.15 seed: 42 training: model_type: "sklearn" # sklearn, pytorch, tensorflow algorithm: "random_forest" params: n_estimators: 100 max_depth: 10 epochs: null # For deep learning models evaluation: metrics: - "accuracy" - "f1_score" - "precision" - "recall" threshold: min_accuracy: 0.85 deployment: enabled: false target: "local" # local, kubernetes, sagemaker logging: level: "INFO"
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