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ai_feedback_collector [2025/05/27 02:11] – [Example 3: Adding Advanced Metrics] eagleeyenebulaai_feedback_collector [2025/05/27 02:16] (current) – [AI Feedback Collector] eagleeyenebula
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 ====== AI Feedback Collector ====== ====== AI Feedback Collector ======
 **[[https://autobotsolutions.com/god/templates/index.1.html|More Developers Docs]]**: **[[https://autobotsolutions.com/god/templates/index.1.html|More Developers Docs]]**:
-The **AI Feedback Collector System** enables the systematic collection of model predictions, user feedback, and related metrics for long-term analysis and refinement of AI systems. By leveraging a structured SQLite database, this system ensures persistence, traceability, and detailed insights for model monitoring, debugging, and optimization over time.+The **AI Feedback Collector System** enables the systematic collection of model predictions, user feedback, and related metrics for long-term analysis and refinement of AI systems. By leveraging a structured **SQLite database**, this system ensures persistence, traceability, and detailed insights for model monitoring, debugging, and optimization over time.
  
 {{youtube>NKMCvl89fPg?large}} {{youtube>NKMCvl89fPg?large}}
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 </code> </code>
 **Explanation**: **Explanation**:
-   * **Input Data**: Represents the data sent to the model (e.g., features for prediction).+   * **Input Data**: Represents the data sent to the model (e.g., **features for prediction**).
    * **Prediction**: Stores model outputs for the input data.    * **Prediction**: Stores model outputs for the input data.
    * **Latency Calculation**: Measures time between prediction start and completion.    * **Latency Calculation**: Measures time between prediction start and completion.
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 </code> </code>
 **Explanation**: **Explanation**:
-   * Identifies cases where the `prediction` field does not match the `actual_value` field.+   * Identifies cases where the **prediction** field does not match the **actual_value** field.
    * Enables targeted debugging for incorrect predictions.    * Enables targeted debugging for incorrect predictions.
 ==== Example 3: Adding Advanced Metrics ==== ==== Example 3: Adding Advanced Metrics ====
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 Integrate feedback logging with an API to log user interactions in real time. Integrate feedback logging with an API to log user interactions in real time.
  
-```python+<code> 
 +python
 from flask import Flask, request, jsonify from flask import Flask, request, jsonify
  
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     )     )
     return jsonify({"message": "Feedback logged successfully"})     return jsonify({"message": "Feedback logged successfully"})
-```+</code>
  
 **Explanation**: **Explanation**:
-- Extends the system into a web-based architecture, enabling client-side applications to log feedback instantly. +    * Extends the system into a web-based architecture, enabling client-side applications to log feedback instantly.
- +
---- +
 ===== Use Cases ===== ===== Use Cases =====
  
 1. **Model Tracking Across Versions**: 1. **Model Tracking Across Versions**:
-   - Identify how performance improves or degrades with version changes.+   * Identify how performance improves or degrades with version changes.
        
 2. **Bias and Drift Monitoring**: 2. **Bias and Drift Monitoring**:
-   - Periodically analyze feedback logs for data or concept drift.+   * Periodically analyze feedback logs for data or concept drift.
  
 3. **Auditing and Compliance**: 3. **Auditing and Compliance**:
-   - Maintain detailed logs for compliance in regulated sectors (e.g., finance, healthcare).+   * Maintain detailed logs for compliance in regulated sectors (e.g., **finance, healthcare**).
  
 4. **Real-Time Error Detection**: 4. **Real-Time Error Detection**:
-   - Detect incorrect predictions while the system is running in production.+   * Detect incorrect predictions while the system is running in production.
  
 5. **Data Pipeline Debugging**: 5. **Data Pipeline Debugging**:
-   - Trace stale or malformed inputs impacting predictions. +   * Trace stale or malformed inputs impacting predictions.
- +
---- +
 ===== Best Practices ===== ===== Best Practices =====
  
 1. **Secure Logging**: 1. **Secure Logging**:
-   - Protect sensitive data attributes in `input_data` and `actual_value` fields during database storage.+   * Protect sensitive data attributes in **input_data** and **actual_value** fields during database storage.
  
 2. **Schema Evolution**: 2. **Schema Evolution**:
-   - Plan for gradual additions to schema by modularizing extensions (e.g., adding metrics or tags as new columns).+   * Plan for gradual additions to schema by modularizing extensions (e.g., adding metrics or tags as new columns).
  
 3. **Performance Optimization**: 3. **Performance Optimization**:
-   - Index frequently queried fields like `model_version` or `timestamp` in the database.+   * Index frequently queried fields like **model_version** or **timestamp** in the database.
  
 4. **Visualization and Aggregation**: 4. **Visualization and Aggregation**:
-   - Use tools like **SQL dashboards** or Python **pandas** for deeper analytics of logged feedback records.+   * Use tools like **SQL dashboards** or Python **pandas** for deeper analytics of logged feedback records.
  
 5. **Regular Maintenance**: 5. **Regular Maintenance**:
-   - Schedule database cleanup jobs to archive older records or large tables. +   * Schedule database cleanup jobs to archive older records or large tables.
- +
---- +
 ===== Conclusion ===== ===== Conclusion =====
  
 The **AI Feedback Collector System** is a powerful tool for maintaining traceable, detailed records of model predictions and performance metrics. By supporting structured feedback logging, model version comparisons, and extensible schemas, it enables robust AI lifecycle management. Whether for debugging, compliance, or optimization, this system ensures practical and scalable feedback collection for modern AI applications. The **AI Feedback Collector System** is a powerful tool for maintaining traceable, detailed records of model predictions and performance metrics. By supporting structured feedback logging, model version comparisons, and extensible schemas, it enables robust AI lifecycle management. Whether for debugging, compliance, or optimization, this system ensures practical and scalable feedback collection for modern AI applications.
ai_feedback_collector.1748311918.txt.gz · Last modified: 2025/05/27 02:11 by eagleeyenebula