# 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)
```bash
# Install from PyPI
pip install ml-assistant-cli
# Or install with cloud support
pip install ml-assistant-cli[cloud]
# Verify installation
mlcli --help
```
#### Option 2: Docker (No Installation Required)
```bash
# Run directly with Docker
docker run -it --rm -v $(pwd):/home/mlcli/workspace santhoshkumar0918/ml-assistant-cli:latest
# Or create convenient alias
alias mlcli="docker run -it --rm -v $(pwd):/home/mlcli/workspace santhoshkumar0918/ml-assistant-cli:latest"
# Then use normally
mlcli --help
```
#### Option 3: pipx (Isolated Installation)
```bash
# Install with pipx for isolated environment
pipx install ml-assistant-cli
mlcli --help
```
#### Option 4: From Source
```bash
# Clone and install
git clone https://github.com/mlcli/mlcli.git
cd mlcli
pip install -e .
```
### Initialize a new ML project
```bash
mlcli init --name my-ml-project
cd my-ml-project
```
### Process your data
```bash
# Add your dataset to data/raw/
mlcli preprocess --input data/raw/your_data.csv --target target_column
```
### Train models
```bash
mlcli train
```
### Evaluate and get suggestions
```bash
mlcli evaluate
mlcli suggest
```
### Make predictions
```bash
mlcli predict --input new_data.csv --output predictions.csv
```
### Deploy to cloud (coming soon)
```bash
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`:
```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 ✅
- [x] Project initialization
- [x] 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](CONTRIBUTING.md) for details.
## 📄 License
MIT License - see [LICENSE](LICENSE) for details.
## 🆘 Support
- 📖 [Documentation](https://mlcli.readthedocs.io)
- 🐛 [Issue Tracker](https://github.com/mlcli/mlcli/issues)
- 💬 [Discussions](https://github.com/mlcli/mlcli/discussions)
---
**Built with ❤️ for the ML community**
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"description": "# ML Assistant CLI \ud83d\ude80\n\n**From dataset to deployed API in minutes**\n\nML 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.\n\n## \u2728 Features\n\n- **End-to-end ML workflow** in a single CLI\n- **AI-guided suggestions** for data quality and model improvements\n- **BentoML integration** for reproducible model packaging\n- **Multi-cloud deployment** (BentoCloud, Azure ML, AWS SageMaker HyperPod)\n- **Production-ready** with monitoring, rollbacks, and traffic management\n- **Beginner-friendly** with sensible defaults and clear guidance\n\n## \ud83d\ude80 Quick Start\n\n### Installation Options\n\n#### Option 1: PyPI (Recommended)\n\n```bash\n# Install from PyPI\npip install ml-assistant-cli\n\n# Or install with cloud support\npip install ml-assistant-cli[cloud]\n\n# Verify installation\nmlcli --help\n```\n\n#### Option 2: Docker (No Installation Required)\n\n```bash\n# Run directly with Docker\ndocker run -it --rm -v $(pwd):/home/mlcli/workspace santhoshkumar0918/ml-assistant-cli:latest\n\n# Or create convenient alias\nalias mlcli=\"docker run -it --rm -v $(pwd):/home/mlcli/workspace santhoshkumar0918/ml-assistant-cli:latest\"\n\n# Then use normally\nmlcli --help\n```\n\n#### Option 3: pipx (Isolated Installation)\n\n```bash\n# Install with pipx for isolated environment\npipx install ml-assistant-cli\nmlcli --help\n```\n\n#### Option 4: From Source\n\n```bash\n# Clone and install\ngit clone https://github.com/mlcli/mlcli.git\ncd mlcli\npip install -e .\n```\n\n### Initialize a new ML project\n\n```bash\nmlcli init --name my-ml-project\ncd my-ml-project\n```\n\n### Process your data\n\n```bash\n# Add your dataset to data/raw/\nmlcli preprocess --input data/raw/your_data.csv --target target_column\n```\n\n### Train models\n\n```bash\nmlcli train\n```\n\n### Evaluate and get suggestions\n\n```bash\nmlcli evaluate\nmlcli suggest\n```\n\n### Make predictions\n\n```bash\nmlcli predict --input new_data.csv --output predictions.csv\n```\n\n### Deploy to cloud (coming soon)\n\n```bash\nmlcli package\nmlcli deploy --provider bentocloud\nmlcli monitor\n```\n\n## \ud83d\udcc1 Project Structure\n\n```\nmy-ml-project/\n\u251c\u2500\u2500 data/\n\u2502 \u251c\u2500\u2500 raw/ # Original datasets\n\u2502 \u251c\u2500\u2500 processed/ # Cleaned data\n\u2502 \u2514\u2500\u2500 external/ # External datasets\n\u251c\u2500\u2500 models/ # Trained models\n\u251c\u2500\u2500 reports/ # Analysis reports\n\u251c\u2500\u2500 deployments/ # Deployment configs\n\u251c\u2500\u2500 mlcli.yaml # Configuration\n\u2514\u2500\u2500 README.md\n```\n\n## \u2699\ufe0f Configuration\n\nCustomize your ML pipeline in `mlcli.yaml`:\n\n```yaml\nproject_name: my-ml-project\ndescription: My awesome ML project\n\ndata:\n target_column: target\n test_size: 0.2\n missing_value_strategy: auto\n scaling_strategy: standard\n\nmodel:\n algorithms: [logistic_regression, random_forest, xgboost]\n hyperparameter_tuning: true\n cv_folds: 5\n\ndeployment:\n provider: bentocloud\n scaling_min: 1\n scaling_max: 3\n instance_type: cpu.2\n```\n\n## \ud83c\udfaf Roadmap\n\n### Phase 1: Local MVP \u2705\n\n- [x] Project initialization\n- [x] Data preprocessing and analysis\n- [ ] Model training with hyperparameter optimization\n- [ ] Model evaluation and metrics\n- [ ] AI-guided suggestions\n- [ ] Batch predictions\n- [ ] BentoML packaging\n\n### Phase 2: Cloud MVP\n\n- [ ] BentoCloud deployment\n- [ ] Model monitoring\n- [ ] Deployment rollbacks\n\n### Phase 3: Multi-Cloud\n\n- [ ] Azure ML integration\n- [ ] AWS SageMaker HyperPod support\n- [ ] Advanced deployment strategies\n- [ ] CI/CD integration\n\n## \ud83e\udd1d Contributing\n\nWe welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.\n\n## \ud83d\udcc4 License\n\nMIT License - see [LICENSE](LICENSE) for details.\n\n## \ud83c\udd98 Support\n\n- \ud83d\udcd6 [Documentation](https://mlcli.readthedocs.io)\n- \ud83d\udc1b [Issue Tracker](https://github.com/mlcli/mlcli/issues)\n- \ud83d\udcac [Discussions](https://github.com/mlcli/mlcli/discussions)\n\n---\n\n**Built with \u2764\ufe0f for the ML community**\n",
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