mlflow-assist 0.2.0

A comprehensive toolkit for ML and LLM development

MLFlow-Assist: Enterprise ML/LLM Development Suite 🚀

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

# 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

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

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

# 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

# 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
• Example Notebooks

🤝 Community & Support

• Report Issues

If you find this project helpful, consider buying me a coffee!

📄 License

MIT License - see LICENSE for details.

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