Model Deployment

Deploying a machine learning model involves moving it from a development environment to a production environment where it can make predictions on new data.

Steps for Model Deployment

Model Exporting

• Use tools like joblib or pickle to serialize the model.

  import joblib
  joblib.dump(model, 'linear_regression_model.pkl')

Deployment Options

• Application Integration: Embed the model into an application for real-time predictions.
• API Deployment: Use frameworks like Flask or FastAPI to create an API endpoint for the model.
• Automated Workflows: Integrate the model into automated data processing pipelines.

Tools and Platforms

• Sklearn Pipeline: Streamline the deployment process by integrating Preprocessing and model steps.
• Gradio: Create user-friendly interfaces for model interaction.

Considerations

• Scalability: Ensure the deployment solution can handle the expected load.
• Model Observability: Implement monitoring to track model performance and detect issues.