- 🤖 AI-Powered Model Creation - Build ML models using natural language descriptions
- 📊 Automated Training - Upload your data and let PlexeAI handle the rest
- ⚡ Async Support - Built-in async interfaces for high-performance applications
- 🔄 Batch Processing - Efficient batch prediction capabilities
- 🛠️ Simple API - Intuitive interface for both beginners and experts
pip install plexe
import plexe # Create a model in seconds model_version = plexe.build( goal="predict customer churn based on usage patterns", model_name="churn-predictor", data_files="customer_data.csv" ) # Make predictions result = plexe.infer( model_name="churn-predictor", model_version=model_version, input_data={ "usage": 100, "tenure": 12, "plan_type": "premium" } )
- 📈 Churn Prediction: Predict customer churn using historical data
- 🏷️ Classification: Categorize text, images, or any structured data
- 📊 Regression: Predict numerical values like sales or pricing
- 🔄 Time Series: Forecast trends and patterns in sequential data
results = plexe.batch_infer( model_name="churn-predictor", model_version=model_version, inputs=[ {"usage": 100, "tenure": 12, "plan_type": "premium"}, {"usage": 50, "tenure": 6, "plan_type": "basic"} ] )
async def main(): model_version = await plexe.abuild( goal="predict customer churn", model_name="churn-predictor", data_files="customer_data.csv" ) result = await plexe.ainfer( model_name="churn-predictor", model_version=model_version, input_data={"usage": 100, "tenure": 12} )
from plexe import PlexeAI with PlexeAI(api_key="your_api_key_here") as client: # Upload data upload_id = client.upload_files("customer_data.csv") # Create and use model model_version = client.build( goal="predict customer churn", model_name="churn-predictor", upload_id=upload_id )
Check out our comprehensive documentation for:
- Detailed API reference
- Advanced usage examples
- Best practices
- Tutorials and guides
# Clone the repository git clone https://github.com/plexe-ai/plexe cd plexe # Install development dependencies pip install -e ".[dev]" # Run tests pytest
We welcome contributions!
This project is licensed under the MIT License - see the LICENSE file for details.
Made with ❤️ by Plexe AI