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ai_pipeline_audit_logger [2025/05/29 12:25] – [Workflow] eagleeyenebulaai_pipeline_audit_logger [2025/05/29 12:47] (current) – [Best Practices] eagleeyenebula
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 ===== Advanced Examples ===== ===== Advanced Examples =====
  
-The following examples illustrate more complex and advanced use cases for `AuditLogger`: +The following examples illustrate more complex and advanced use cases for **AuditLogger**:
- +
---- +
 ==== Example 1: Auditing a Complete Pipeline Workflow ==== ==== Example 1: Auditing a Complete Pipeline Workflow ====
  
 Track key stages in a typical pipeline lifecycle: Track key stages in a typical pipeline lifecycle:
-```python+<code> 
 +python
 audit_logger = AuditLogger() audit_logger = AuditLogger()
  
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         status="FAILURE"         status="FAILURE"
     )     )
-``` +</code>
- +
---- +
 ==== 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 =====
  
 The **AI Pipeline Audit Logger** is a powerful and lightweight tool for maintaining robust and structured observability in AI workflows. By logging critical events with actionable insights, it enhances pipeline monitoring, compliance, and reliability. Its extensibility ensures that it can be adapted for unique operational challenges while promoting best practices in logging and audit trails. The **AI Pipeline Audit Logger** is a powerful and lightweight tool for maintaining robust and structured observability in AI workflows. By logging critical events with actionable insights, it enhances pipeline monitoring, compliance, and reliability. Its extensibility ensures that it can be adapted for unique operational challenges while promoting best practices in logging and audit trails.
 +
 +Designed with clarity and performance in mind, the logger integrates seamlessly into existing AI systems, capturing essential runtime data without introducing unnecessary overhead. Whether you're managing data preprocessing, model training, or deployment, the tool offers a consistent and configurable approach to auditing. Developers can customize logging levels, formats, and storage targets to align with organizational needs enabling full-lifecycle visibility and fostering a culture of responsible AI development.
ai_pipeline_audit_logger.1748521551.txt.gz · Last modified: 2025/05/29 12:25 by eagleeyenebula