mlflow-assist 0.2.0
A comprehensive toolkit for ML and LLM development
License: MIT
Requires Python: >=3.8
Keywords: machine-learning, deep-learning, llm, mlops, automl, gpt, model-optimization, mlflow, enterprise-ai, ai-automation, model-monitoring, deployment, pytorch, tensorflow, prompt-engineering

# MLFlow-Assist: Enterprise ML/LLM Development Suite 🚀

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A comprehensive enterprise-ready toolkit that supercharges your ML and LLM development workflow with automated optimization, deployment, monitoring, and monetization capabilities.

## 🌟 Key Features

### AutoML & Model Management
- 🤖 Automated model selection and optimization
- 📊 Hyperparameter optimization with Optuna
- 🔧 Model compression (pruning, quantization, distillation)
- 🚀 Distributed training with multi-GPU support

### LLM Capabilities
- 🧠 Advanced prompt engineering and chain management
- 🔄 Multi-step reasoning chains
- 💬 Conversation history management
- 🎯 Context-aware processing

### Enterprise Features
- 💰 Usage tracking and monetization
- 📊 Real-time performance monitoring
- 🔄 Automated deployment (K8s/Docker)
- 📈 Model drift detection & alerts

## 🚀 Installation

```bash
# From PyPI (recommended)
pip install mlflow-assist

# Latest development version from GitHub
pip install git+https://github.com/happyvibess/mlflow-assist.git
```

## 🌟 Quick Start in 5 Minutes

### 1. Simple Classification
```python
from mlflow_assist.advanced.automl import AutoML, AutoMLConfig

# Automated model selection and optimization
automl = AutoML(AutoMLConfig(task_type="classification"))
best_model = automl.optimize(X_train, y_train)

# Model compression and optimization
from mlflow_assist.advanced.optimization import ModelOptimizer
optimizer = ModelOptimizer(compression_method="quantization")
optimized_model = optimizer.optimize(model)
```

### 2. Text Generation with LLMs
```python
from mlflow_assist.advanced.llm_chains import LLMChain

chain = LLMChain("gpt-3.5-turbo")
chain.add_prompt_template("""
Context: {context}
Question: {question}
Answer:""")

# Execute multi-step chains
pipeline = chain.create_chain([
    {"template": "Summarize: {text}", "use_response_as_input": True},
    {"template": "Extract key points: {text}"}
])
```

### 3. Production Features
```python
# Usage tracking and monetization
from mlflow_assist.enterprise.monetization import EnterpriseManager
manager = EnterpriseManager(subscription_plan="pro")
manager.track_usage("api_calls")

# Performance monitoring
from mlflow_assist.enterprise.monitoring import PerformanceMonitor
monitor = PerformanceMonitor()
metrics = monitor.analyze_performance(timeframe="1h")

# Automated deployment
from mlflow_assist.enterprise.deployment import DeploymentManager
deployer = DeploymentManager()
deployer.deploy(model, deployment_type="kubernetes")
```

## 👩‍💻 Development Setup

```bash
# Clone the repository
git clone https://github.com/happyvibess/mlflow-assist.git
cd mlflow-assist

# Install in development mode with all extras
pip install -e ".[dev]"

# Run tests
pytest
```

## 📚 Documentation & Resources

- [Getting Started Guide](docs/getting-started.md)
- [Example Notebooks](examples/notebooks/)

## 🤝 Community & Support

- [Report Issues](https://github.com/happyvibess/mlflow-assist/issues)

If you find this project helpful, consider [buying me a coffee](https://www.buymeacoffee.com/happyvibess)!

## 📄 License

MIT License - see [LICENSE](LICENSE) for details.

---

<p align="center">
Made with ❤️ by MLFlow-Assist Team | <a href="https://www.buymeacoffee.com/happyvibess">Support the Project</a>
</p>