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 FeedbackCollector class serves as the foundation of the system, streamlining the process of logging prediction data, model performance, and relevant metrics (e.g., latency).

The AI Feedback Collector System addresses several critical needs in AI workflows:

• Traceable Logging: Store predictions and input-output data for reproducibility and debugging.
• Error Analysis: Detect discrepancies between predictions and actual values for model refinement.
• Performance Monitoring: Track critical metrics, such as latency, across model versions.
• Version Comparison: Maintain feedback logs segregated by model versions for cross-version evaluation.
• Scalability: Enable long-term feedback and prediction data storage for subsequent benchmarking.

By integrating this system, practitioners can streamline and automate feedback collection pipelines, ensuring AI systems remain robust, explainable, and aligned with user expectations over time.

1. SQLite-Powered Feedback Logging:

• Stores all feedback in a structured database (feedback.db) for persistence and easy querying.

2. Rich Metadata:

• Tracks the following attributes for each feedback entry:
  • Input Data
  • Model Prediction
  • Actual Value (Ground Truth)
  • Model Version
  • Timestamp
  • Latency (time taken for prediction)

3. Reproducibility and Debugging:

• Links input data and predictions seamlessly for debugging and model performance evaluation.

4. Model Versioning:

• Allows version-specific logging for comparing the performance of different model iterations.

5. Ease of Integration:

• Minimal integration effort required; extendable to new use cases, metrics, or systems.

The FeedbackCollector class handles database initialization, feedback logging, and error handling to ensure robust operation.

python
import sqlite3
import logging
from datetime import datetime

class FeedbackCollector:
    """
    Logs pipeline predictions and metrics for long-term analysis and refinement.
    """

    def __init__(self, db_path="feedback.db"):
        self.db_path = db_path
        self._initialize_database()

    def _initialize_database(self):
        """
        Initializes the SQLite database for feedback collection.
        """
        try:
            with sqlite3.connect(self.db_path) as conn:
                cursor = conn.cursor()
                cursor.execute('''CREATE TABLE IF NOT EXISTS feedback (
                                    id INTEGER PRIMARY KEY,
                                    timestamp TEXT,
                                    input_data TEXT,
                                    prediction TEXT,
                                    actual_value TEXT,
                                    model_version TEXT,
                                    latency REAL
                                  )''')
                conn.commit()
        except Exception as e:
            logging.error(f"Failed to initialize feedback database: {e}")

    def log_feedback(self, input_data, prediction, actual_value, model_version, latency):
        """
        Logs feedback into the database.
        """
        timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
        try:
            with sqlite3.connect(self.db_path) as conn:
                cursor = conn.cursor()
                cursor.execute(
                    '''INSERT INTO feedback (timestamp, input_data, prediction, actual_value, model_version, latency)
                       VALUES (?, ?, ?, ?, ?, ?)''',
                    (timestamp, str(input_data), str(prediction), str(actual_value), model_version, latency)
                )
                conn.commit()
                logging.info("Feedback logged successfully.")
        except Exception as e:
            logging.error(f"Failed to log feedback: {e}")

Inputs:

• input_data: Features provided to the model for prediction.
• prediction: Model-generated output based on input_data.
• actual_value: Ground truth value to evaluate the prediction.
• model_version: The version identifier of the AI model.
• latency: Total time taken for model prediction (in seconds).

* Database Schema:

• Table: feedback
  • id: Primary key (integer)
  • timestamp: Timestamp of feedback logging (text)
  • input_data: Serialized representation of features (text)
  • prediction: Serialized model prediction (text)
  • actual_value: Serialized ground truth values (text)
  • model_version: Model version identifier (text)
  • latency: Latency of the prediction (real)

Here are some advanced usage examples to demonstrate the system's capabilities.

In this example, we log model predictions along with their input data, actual values, and latency.

python
from ai_feedback_collector import FeedbackCollector
import time

Simulated objects

model = ...  # Trained model
test_data = ...  # Test data as input
true_labels = ...  # Actual ground truth
model_version = "v1.0"

Initialize FeedbackCollector

feedback_collector = FeedbackCollector()

Log predictions and related feedback

start_time = time.time()
predictions = model.predict(test_data)
end_time = time.time()

feedback_collector.log_feedback(
    input_data=test_data,
    prediction=predictions,
    actual_value=true_labels,
    model_version=model_version,
    latency=end_time - start_time
)

Explanation:

• Input Data: Represents the data sent to the model (e.g., features for prediction).
• Prediction: Stores model outputs for the input data.
• Latency Calculation: Measures time between prediction start and completion.

Use the stored feedback to retrieve and analyze cases where predictions failed or were incorrect.

python
import sqlite3

Retrieve incorrect predictions from the database

def fetch_errors(db_path="feedback.db"):
    with sqlite3.connect(db_path) as conn:
        cursor = conn.cursor()
        query = '''SELECT * FROM feedback WHERE prediction != actual_value'''
        cursor.execute(query)
        return cursor.fetchall()

Fetch and print incorrect feedback logs

errors = fetch_errors()
for error in errors:
    print(f"Error Entry: {error}")

Explanation:

• Identifies cases where the prediction field does not match the actual_value field.
• Enables targeted debugging for incorrect predictions.

The system can be extended by adding fields like confidence scores, user feedback, or error ratings.

python
class ExtendedFeedbackCollector(FeedbackCollector):
    def _initialize_database(self):
        """
        Extends the database schema to include confidence scores.
        """
        try:
            with sqlite3.connect(self.db_path) as conn:
                cursor = conn.cursor()
                cursor.execute('''CREATE TABLE IF NOT EXISTS feedback (
                                    id INTEGER PRIMARY KEY,
                                    timestamp TEXT,
                                    input_data TEXT,
                                    prediction TEXT,
                                    actual_value TEXT,
                                    model_version TEXT,
                                    latency REAL,
                                    confidence_score REAL
                                  )''')
                conn.commit()
        except Exception as e:
            logging.error(f"Failed to initialize extended feedback database: {e}")

    def log_feedback(self, input_data, prediction, actual_value, model_version, latency, confidence_score):
        """
        Logs feedback with additional metrics like confidence scores.
        """
        timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
        try:
            with sqlite3.connect(self.db_path) as conn:
                cursor = conn.cursor()
                cursor.execute(
                    '''INSERT INTO feedback (timestamp, input_data, prediction, actual_value, model_version, latency, confidence_score)
                       VALUES (?, ?, ?, ?, ?, ?, ?)''',
                    (timestamp, str(input_data), str(prediction), str(actual_value), model_version, latency, confidence_score)
                )
                conn.commit()
                logging.info("Extended feedback logged successfully.")
        except Exception as e:
            logging.error(f"Failed to log extended feedback: {e}")

Integrate feedback logging with an API to log user interactions in real time.

python
from flask import Flask, request, jsonify

app = Flask(__name__)
feedback_collector = FeedbackCollector()

@app.route('/log-feedback', methods=['POST'])
def log_feedback():
    data = request.json
    feedback_collector.log_feedback(
        input_data=data['input_data'],
        prediction=data['prediction'],
        actual_value=data.get('actual_value', None),
        model_version=data['model_version'],
        latency=data['latency']
    )
    return jsonify({"message": "Feedback logged successfully"})

Explanation:

• Extends the system into a web-based architecture, enabling client-side applications to log feedback instantly.

1. Model Tracking Across Versions:

• Identify how performance improves or degrades with version changes.

2. Bias and Drift Monitoring:

• Periodically analyze feedback logs for data or concept drift.

3. Auditing and Compliance:

• Maintain detailed logs for compliance in regulated sectors (e.g., finance, healthcare).

4. Real-Time Error Detection:

• Detect incorrect predictions while the system is running in production.

5. Data Pipeline Debugging:

• Trace stale or malformed inputs impacting predictions.

1. Secure Logging:

• Protect sensitive data attributes in input_data and actual_value fields during database storage.

2. Schema Evolution:

• Plan for gradual additions to schema by modularizing extensions (e.g., adding metrics or tags as new columns).

3. Performance Optimization:

• Index frequently queried fields like model_version or timestamp in the database.

4. Visualization and Aggregation:

• Use tools like SQL dashboards or Python pandas for deeper analytics of logged feedback records.

5. Regular Maintenance:

• Schedule database cleanup jobs to archive older records or large tables.

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.