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ai_pipeline_audit_logger

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ai_pipeline_audit_logger [2025/05/29 12:42] – [Advanced Examples] eagleeyenebulaai_pipeline_audit_logger [2025/05/29 12:47] (current) – [Best Practices] eagleeyenebula
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 ==== Example 2: Drift Detection and Handling ==== ==== Example 2: Drift Detection and Handling ====
  
-Monitor and log drift detection events: +**Monitor and log drift detection events:** 
-```python+<code> 
 +python
 def monitor_drift(data): def monitor_drift(data):
     drift_detected = check_drift(data)     drift_detected = check_drift(data)
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     else:     else:
         audit_logger.log_event("No Drift Detected", status="INFO")         audit_logger.log_event("No Drift Detected", status="INFO")
 +</code>
  
-# Schedule drift monitoring+**Schedule drift monitoring** 
 +<code>
 audit_logger.log_event("Drift Monitoring initiated") audit_logger.log_event("Drift Monitoring initiated")
 monitor_drift(data) monitor_drift(data)
-``` +</code>
- +
----+
  
 ==== Example 3: Structured Logging to External Systems ==== ==== Example 3: Structured Logging to External Systems ====
  
-Extend `AuditLogger` to send logs to an external database or observability tool: +Extend **AuditLogger** to send logs to an external database or observability tool: 
-```python+<code> 
 +python
 class ExternalAuditLogger(AuditLogger): class ExternalAuditLogger(AuditLogger):
     def __init__(self, db_connection):     def __init__(self, db_connection):
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         super().log_event(event_name, details, status)         super().log_event(event_name, details, status)
         self.db_connection.write({"event": event_name, "details": details, "status": status})         self.db_connection.write({"event": event_name, "details": details, "status": status})
- +</code> 
-# Sample usage+**Sample usage** 
 +<code>
 db_connection = MockDatabaseConnection() db_connection = MockDatabaseConnection()
 audit_logger = ExternalAuditLogger(db_connection) audit_logger = ExternalAuditLogger(db_connection)
  
 audit_logger.log_event("Model deployment successful", details={"version": "1.0.1"}, status="INFO") audit_logger.log_event("Model deployment successful", details={"version": "1.0.1"}, status="INFO")
-``` +</code>
- +
----+
  
 ==== Example 4: Automated Anomaly Reporting ==== ==== Example 4: Automated Anomaly Reporting ====
  
-Automatically flag anomalies in pipeline execution: +**Automatically flag anomalies in pipeline execution:** 
-```python+<code> 
 +python
 def detect_anomaly(metrics): def detect_anomaly(metrics):
     if metrics["accuracy"] < 0.8:     if metrics["accuracy"] < 0.8:
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             status="WARNING"             status="WARNING"
         )         )
- +</code> 
-# Example anomaly detection+**Example anomaly detection** 
 +<code>
 results = {"accuracy": 0.75} results = {"accuracy": 0.75}
 detect_anomaly(results) detect_anomaly(results)
-``` +</code>
- +
----+
  
 ===== Extending the Framework ===== ===== Extending the Framework =====
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 The **AuditLogger** is designed to be highly extensible for custom and domain-specific requirements. The **AuditLogger** is designed to be highly extensible for custom and domain-specific requirements.
  
-### 1. Custom Status Codes +1. Custom Status Codes 
-Extend the logger to support additional status categories: +   * Extend the logger to support additional status categories: 
-```python+<code> 
 +python
 class ExtendedAuditLogger(AuditLogger): class ExtendedAuditLogger(AuditLogger):
     VALID_STATUSES = ["INFO", "WARNING", "FAILURE", "CRITICAL"]     VALID_STATUSES = ["INFO", "WARNING", "FAILURE", "CRITICAL"]
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             raise ValueError(f"Invalid status: {status}")             raise ValueError(f"Invalid status: {status}")
         super().log_event(event_name, details, status)         super().log_event(event_name, details, status)
-```+</code>
  
----+2. Integration with Observability Platforms 
 +   * Push logs to third-party observability tools like Prometheus, Grafana, or Splunk.
  
-### 2. Integration with Observability Platforms +**Example:** 
-Push logs to third-party observability tools like Prometheus, Grafana, or Splunk. +<code> 
- +python
-Example: +
-```python+
 import requests import requests
  
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             "event": event_name, "details": details, "status": status             "event": event_name, "details": details, "status": status
         })         })
-``` +</code>
- +
----+
  
 ===== Best Practices ===== ===== Best Practices =====
  
 1. **Define Clear Log Levels:**   1. **Define Clear Log Levels:**  
-   Use consistent log statuses (e.g., `INFO`, `WARNING`, `FAILURE`) to facilitate pipeline observability and debugging.+   * Use consistent log statuses (e.g., **INFO**, **WARNING**, **FAILURE**) to facilitate pipeline observability and debugging.
  
 2. **Enrich Logs with Context:**   2. **Enrich Logs with Context:**  
-   Always include additional `details` to provide actionable information to downstream systems or engineers.+   * Always include additional `details` to provide actionable information to downstream systems or engineers.
  
 3. **Enable Structured Logging:**   3. **Enable Structured Logging:**  
-   Use structured formats (e.g., JSON) for easier parsing, searching, and integration with external systems.+   * Use structured formats (e.g., JSON) for easier parsing, searching, and integration with external systems.
  
 4. **Monitor and Alert in Real Time:**   4. **Monitor and Alert in Real Time:**  
-   Integrate log messages into monitoring frameworks to enable proactive alerts.+   * Integrate log messages into monitoring frameworks to enable proactive alerts.
  
 5. **Extend for Domain-Specific Needs:**   5. **Extend for Domain-Specific Needs:**  
-   Develop custom child classes for unique pipeline scenarios like anomaly detection or multi-pipeline orchestration. +   * Develop custom child classes for unique pipeline scenarios like anomaly detection or multi-pipeline orchestration.
- +
----+
  
 ===== Conclusion ===== ===== Conclusion =====
ai_pipeline_audit_logger.1748522562.txt.gz · Last modified: 2025/05/29 12:42 by eagleeyenebula