A machine learning project following the MLE-Star methodology (Model design, Learning pipeline, Evaluation, Systematic testing, Training optimization, Analysis validation, Refinement deployment).

ml-experiment/
├── data/                    # Data storage
│   ├── raw/                # Original, immutable data
│   ├── processed/          # Cleaned and processed data
│   └── external/           # Third-party datasets
├── models/                 # Trained models and artifacts
├── notebooks/              # Jupyter notebooks for exploration
│   ├── 01_model_design.ipynb
│   ├── 02_training_pipeline.ipynb
│   ├── 03_model_evaluation.ipynb
│   ├── 04_hyperparameter_tuning.ipynb
│   ├── 05_model_analysis.ipynb
│   └── 06_deployment.ipynb
├── src/                    # Source code
│   ├── data/              # Data processing modules
│   ├── features/          # Feature engineering
│   ├── models/            # Model definitions and training
│   ├── visualization/     # Visualization utilities
│   └── api/               # API endpoints for serving
├── tests/                  # Unit tests
├── configs/                # Configuration files
├── outputs/                # Generated outputs
│   ├── models/            # Saved models
│   ├── figures/           # Generated plots
│   └── reports/           # Analysis reports
├── requirements.txt        # Python dependencies
├── config.yaml            # Main configuration
└── README.md              # This file

• Define problem statement and success metrics
• Select appropriate ML algorithms and architectures
• Design model architecture and components

• Implement data preprocessing and feature engineering
• Create training and validation pipelines
• Set up data loaders and transformation pipelines

• Define evaluation metrics and validation strategies
• Implement comprehensive model evaluation
• Create performance monitoring and reporting

• Implement unit tests for all components
• Create integration tests for pipelines
• Add data validation and model testing

• Implement hyperparameter tuning
• Optimize training procedures and schedules
• Add model selection and ensemble methods

• Perform model interpretability analysis
• Validate model assumptions and behavior
• Generate comprehensive analysis reports

• Refine model based on analysis results
• Prepare model for deployment
• Create deployment infrastructure and monitoring

1. Install Dependencies

   pip install -r requirements.txt

2. Configure Project Edit configs/config.yaml with your specific settings.

3. Run MLE-Star Workflow

   # Initialize project (already done)
   claude-flow automation mle-star status
   
   # Run individual stages
   claude-flow automation mle-star stage model_design
   claude-flow automation mle-star stage learning_pipeline
   
   # Or run complete workflow
   claude-flow automation mle-star run

This project is configured to use pytorch as the primary ML framework.

{{#if (eq mlFramework "pytorch")}} PyTorch Configuration:

• Version: 1.9.0+
• GPU Support: Available if CUDA is installed
• Key Components: torch, torchvision, torch.nn, torch.optim

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader

{{/if}}

{{#if (eq mlFramework "tensorflow")}} TensorFlow Configuration:

• Version: 2.6.0+
• GPU Support: Available if CUDA is installed
• Key Components: tf.keras, tf.data, tf.nn

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

{{/if}}

{{#if (eq mlFramework "scikit-learn")}} Scikit-Learn Configuration:

• Version: 1.0.0+
• CPU-optimized machine learning
• Key Components: sklearn.model_selection, sklearn.metrics

from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from sklearn.ensemble import RandomForestClassifier

{{/if}}

# Model design stage
claude-flow automation mle-star stage model_design --framework pytorch

# Training pipeline stage
claude-flow automation mle-star stage learning_pipeline

# Model evaluation
claude-flow automation mle-star stage evaluation_setup

# Run all stages
claude-flow automation mle-star run --continue-on-error

# Check status
claude-flow automation mle-star status

# Validate environment
claude-flow automation mle-star validate

# Deploy as API
claude-flow automation mle-star deploy --service api

# Deploy with Docker
claude-flow automation mle-star deploy --service docker

The main configuration is in configs/config.yaml. Key settings include:

• Data paths: Input and output data directories
• Model parameters: Architecture and hyperparameters
• Training settings: Batch size, epochs, learning rate
• Evaluation metrics: Performance measurement criteria

1. Data Exploration → notebooks/01_model_design.ipynb
2. Pipeline Development → notebooks/02_training_pipeline.ipynb
3. Model Training → src/models/train.py
4. Evaluation → notebooks/03_model_evaluation.ipynb
5. Optimization → notebooks/04_hyperparameter_tuning.ipynb
6. Analysis → notebooks/05_model_analysis.ipynb
7. Deployment → notebooks/06_deployment.ipynb

Run tests with:

pytest tests/

Generate coverage report:

pytest --cov=src tests/

1. Follow MLE-Star methodology for all changes
2. Add tests for new functionality
3. Update documentation
4. Run validation before submitting

[Specify your license here]

Generated with MLE-Star methodology for systematic ML development.