Deploy Your Model

After defining your schemas and uploading data, you're ready to deploy your model. Deployment makes your AI model available for processing data and integrating with other systems.

Steps:

1. Train the Model:

   • Go to the "Model Training" section.

   • Select your input and output schemas.

   • Choose a machine learning algorithm or let the platform select the best one.

   • Click "Train Model."

2. Monitor Training Progress:

   • View real-time updates on training status and performance metrics.

   • Metrics Include: Accuracy, Precision, Recall, F1 Score.

3. Evaluate Model Performance:

   • After training completes, assess whether the model meets your desired outcomes.

   • Tip: If performance is not satisfactory, consider refining your data or adjusting your schemas.

4. Deploy the Model:

   • Once satisfied, click "Deploy Model."

   • Assign a version name for tracking (e.g., "damage_assessment_v1").

5. Configure Deployment Settings:

   • Scaling Options: Set parameters for handling different loads.

   • Access Control: Define who can access the model.

6. Test the Deployed Model:

   • Use sample data to test predictions.

   • Ensure outputs align with expectations.

What This Does:

• Makes the Model Accessible: Allows you and authorized users to interact with the model via API or web interface.

• Enables Integration: Integrate the model into your workflows or applications.