Ml Pipeline Starter

Workflows

Build and deploy production ML pipelines with data processing, model training, evaluation, and deployment using TensorFlow, PyTorch, or Scikit-learn.

Install

openclaw skills install ml-pipeline-starter

ML Pipeline Starter

Build production ML pipelines.

Features

Data Processing

  • Data validation
  • Feature engineering
  • Data augmentation

Model Training

  • Hyperparameter tuning
  • Cross-validation
  • Model versioning

Evaluation

  • Metrics tracking
  • Bias detection
  • Performance monitoring

Deployment

  • Model serving
  • A/B testing
  • Rollback support

Quick Start

# Create pipeline
./ml-pipeline.sh create my-model

# Train
./ml-pipeline.sh train my-model

# Deploy
./ml-pipeline.sh deploy my-model production

Frameworks

  • TensorFlow
  • PyTorch
  • Scikit-learn

Requirements

  • Python 3.8+
  • Docker

Author

Sunshine-del-ux