ml-assistant-cli 0.1.0

End-to-end ML workflow CLI - from dataset to deployed API

ML Assistant CLI 🚀

From dataset to deployed API in minutes

ML Assistant CLI is a developer-first command-line tool that unifies the entire ML lifecycle - from data preprocessing to cloud deployment - with AI-guided suggestions and one-click deployments.

✨ Features

• End-to-end ML workflow in a single CLI
• AI-guided suggestions for data quality and model improvements
• BentoML integration for reproducible model packaging
• Multi-cloud deployment (BentoCloud, Azure ML, AWS SageMaker HyperPod)
• Production-ready with monitoring, rollbacks, and traffic management
• Beginner-friendly with sensible defaults and clear guidance

🚀 Quick Start

Installation Options

Option 1: PyPI (Recommended)

# Install from PyPI
pip install mlcli

# Or install with cloud support
pip install mlcli[cloud]

# Verify installation
mlcli --help

Option 2: Docker (No Installation Required)

# Run directly with Docker
docker run -it --rm -v $(pwd):/home/mlcli/workspace mlcli/mlcli:latest

# Or create convenient alias
alias mlcli="docker run -it --rm -v \$(pwd):/home/mlcli/workspace mlcli/mlcli:latest"

# Then use normally
mlcli --help

Option 3: pipx (Isolated Installation)

# Install with pipx for isolated environment
pipx install mlcli
mlcli --help

Option 4: From Source

# Clone and install
git clone https://github.com/mlcli/mlcli.git
cd mlcli
pip install -e .

Option 4: pipx (Isolated Installation)

# Install with pipx for isolated environment
pipx install mlcli

Initialize a new ML project

mlcli init --name my-ml-project
cd my-ml-project

Process your data

# Add your dataset to data/raw/
mlcli preprocess --input data/raw/your_data.csv --target target_column

Train models

mlcli train

Evaluate and get suggestions

mlcli evaluate
mlcli suggest

Make predictions

mlcli predict --input new_data.csv --output predictions.csv

Deploy to cloud (coming soon)

mlcli package
mlcli deploy --provider bentocloud
mlcli monitor

📁 Project Structure

my-ml-project/
├── data/
│   ├── raw/          # Original datasets
│   ├── processed/    # Cleaned data
│   └── external/     # External datasets
├── models/           # Trained models
├── reports/          # Analysis reports
├── deployments/      # Deployment configs
├── mlcli.yaml       # Configuration
└── README.md

⚙️ Configuration

Customize your ML pipeline in mlcli.yaml:

project_name: my-ml-project
description: My awesome ML project

data:
  target_column: target
  test_size: 0.2
  missing_value_strategy: auto
  scaling_strategy: standard

model:
  algorithms: [logistic_regression, random_forest, xgboost]
  hyperparameter_tuning: true
  cv_folds: 5

deployment:
  provider: bentocloud
  scaling_min: 1
  scaling_max: 3
  instance_type: cpu.2

🎯 Roadmap

Phase 1: Local MVP ✅

• Project initialization
• Data preprocessing and analysis
• Model training with hyperparameter optimization
• Model evaluation and metrics
• AI-guided suggestions
• Batch predictions
• BentoML packaging

Phase 2: Cloud MVP

• BentoCloud deployment
• Model monitoring
• Deployment rollbacks

Phase 3: Multi-Cloud

• Azure ML integration
• AWS SageMaker HyperPod support
• Advanced deployment strategies
• CI/CD integration

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

📄 License

MIT License - see LICENSE for details.

🆘 Support

• 📖 Documentation
• 🐛 Issue Tracker
• 💬 Discussions

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