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ai_interface_perdiction

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ai_interface_perdiction [2025/05/27 20:14] – [Example 3: Adding Input Validation] eagleeyenebulaai_interface_perdiction [2025/05/27 20:17] (current) – [Best Practices] eagleeyenebula
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 This example improves the interface by introducing batch processing. This example improves the interface by introducing batch processing.
  
-```python+<code> 
 +python
 class BatchPredictionInterface(PredictionInterface): class BatchPredictionInterface(PredictionInterface):
     """     """
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             all_predictions.append(self.handle_prediction_request(batch))             all_predictions.append(self.handle_prediction_request(batch))
         return all_predictions         return all_predictions
 +</code>
  
- +**Usage** 
-# Usage+<code>
 interface = BatchPredictionInterface(None) interface = BatchPredictionInterface(None)
 batch_data = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] batch_data = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
- +</code> 
-# Perform batch predictions+**Perform batch predictions** 
 +<code>
 batch_results = interface.batch_predictions(batch_data) batch_results = interface.batch_predictions(batch_data)
 print("Batch Predictions:", batch_results) print("Batch Predictions:", batch_results)
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 # INFO:root:Processing batch: [4, 5, 6] # INFO:root:Processing batch: [4, 5, 6]
 # INFO:root:Processing batch: [7, 8, 9] # INFO:root:Processing batch: [7, 8, 9]
-```+</code>
  
 **Explanation**: **Explanation**:
-- Designed for scenarios requiring predictions over multiple datasets in a single operation. +   * Designed for scenarios requiring predictions over multiple datasets in a single operation.
- +
---- +
 ==== Example 5: Persistent Prediction Results ==== ==== Example 5: Persistent Prediction Results ====
  
 Save prediction results to a file for further analysis. Save prediction results to a file for further analysis.
  
-```python+<code> 
 +python
 import json import json
  
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             json.dump(predictions, file)             json.dump(predictions, file)
         logging.info(f"Predictions saved to {filename}.")         logging.info(f"Predictions saved to {filename}.")
 +</code>
  
- +**Usage** 
-# Usage+<code>
 interface = PersistentPredictionInterface(None) interface = PersistentPredictionInterface(None)
 predictions = interface.handle_prediction_request([1, 2, 3]) predictions = interface.handle_prediction_request([1, 2, 3])
 interface.save_predictions(predictions, "predictions.json") interface.save_predictions(predictions, "predictions.json")
-```+</code>
  
 **Explanation**: **Explanation**:
-- Ensures prediction results can be stored and loaded later by saving them in a JSON file. +   * Ensures prediction results can be stored and loaded later by saving them in a JSON file.
- +
---- +
 ===== Use Cases ===== ===== Use Cases =====
  
 1. **Real-Time Model Serving**:   1. **Real-Time Model Serving**:  
-   Create a prediction-serving pipeline for real-time applications (e.g., APIs).+   * Create a prediction-serving pipeline for real-time applications (e.g., APIs).
  
 2. **Batch Prediction Systems**:   2. **Batch Prediction Systems**:  
-   Efficiently process batch inputs for large datasets.+   * Efficiently process batch inputs for large datasets.
  
 3. **Data Validation Before Inference**:   3. **Data Validation Before Inference**:  
-   Ensure input data meets pre-defined conditions (e.g., type checks or range validation).+   * Ensure input data meets pre-defined conditions (e.g., type checks or range validation).
  
 4. **Logging and Debugging Predictions**:   4. **Logging and Debugging Predictions**:  
-   Leverage integrated logging to identify issues during the prediction process.+   * Leverage integrated logging to identify issues during the prediction process.
  
 5. **Persistent Predictions**:   5. **Persistent Predictions**:  
-   Save results for offline analysis or inclusion in reporting pipelines. +   * Save results for offline analysis or inclusion in reporting pipelines.
- +
---- +
 ===== Best Practices ===== ===== Best Practices =====
  
 1. **Validate Input Data**:   1. **Validate Input Data**:  
-   Always validate input data before feeding it to machine learning models.+   * Always validate input data before feeding it to machine learning models.
  
 2. **Implement Error Handling**:   2. **Implement Error Handling**:  
-   Account for potential prediction errors or invalid inputs.+   * Account for potential prediction errors or invalid inputs.
  
 3. **Optimize for Batch Processing**:   3. **Optimize for Batch Processing**:  
-   Use batch predictions to improve efficiency for applications involving large datasets.+   * Use batch predictions to improve efficiency for applications involving large datasets.
  
 4. **Leverage Logging**:   4. **Leverage Logging**:  
-   Enable detailed logging for easier debugging and transparency in prediction outputs.+   * Enable detailed logging for easier debugging and transparency in prediction outputs.
  
 5. **Integrate with Real Models**:   5. **Integrate with Real Models**:  
-   Replace mock logic with actual AI/ML models for robust production-ready systems. +   * Replace mock logic with actual AI/ML models for robust production-ready systems.
- +
---- +
 ===== Conclusion ===== ===== Conclusion =====
  
ai_interface_perdiction.1748376881.txt.gz · Last modified: 2025/05/27 20:14 by eagleeyenebula