Generalized Omni-dimensional Development

User Tools

Site Tools

Trace:
ai_pipeline_audit_logger

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revisionPrevious revision
Next revision		Previous revision
ai_pipeline_audit_logger [2025/05/29 12:21] – [Class Overview] eagleeyenebulaai_pipeline_audit_logger [2025/05/29 12:47] (current) – [Best Practices] eagleeyenebula
Line 55: Line 55:
     :param event_name: Name or description of the event being logged (e.g., 'Data Ingestion started').     :param event_name: Name or description of the event being logged (e.g., 'Data Ingestion started').
     :param details: Dictionary containing additional context or information about the event.     :param details: Dictionary containing additional context or information about the event.
-    :param status: Severity of the event. Options: 'INFO', 'WARNING', 'FAILURE'.+    :param status: Severity of the event. Options: '**INFO**', '**WARNING**', '**FAILURE**'.
     """     """
     pass     pass
Line 67: Line 67:
   * **event_name** (str): Descriptive name of the event.   * **event_name** (str): Descriptive name of the event.
   * **details** (dict, Optional): Any additional information to include with the log (e.g., row counts, error messages).   * **details** (dict, Optional): Any additional information to include with the log (e.g., row counts, error messages).
-  * **status** (str, Optional): Event status indicating severity. Defaults to `"INFO"`.+  * **status** (str, Optional): Event status indicating severity. Defaults to "**INFO**".
     * Options: "**INFO**", "**WARNING**", "**FAILURE**".     * Options: "**INFO**", "**WARNING**", "**FAILURE**".
  
Line 89: Line 89:
 ===== Workflow ===== ===== Workflow =====
  
-### Step-by-Step Workflow for Using AuditLogger+**Step-by-Step Workflow for Using AuditLogger**
  
 1. **Initialize the Logger** 1. **Initialize the Logger**
-   Create an instance of the `AuditLogger` class: +   * Create an instance of the **AuditLogger** class: 
-   ```python+<code> 
 +   python
    audit_logger = AuditLogger()    audit_logger = AuditLogger()
-   ```+</code>   
  
 2. **Log Events** 2. **Log Events**
-   Track each stage in your pipeline by calling the `log_event` method with appropriate parameters.+   * Track each stage in your pipeline by calling the **log_event** method with appropriate parameters.
  
-   Example: +**Example:** 
-   ```python+<code> 
 +   python
    audit_logger.log_event("Model Training Started")    audit_logger.log_event("Model Training Started")
-   ```+</code>   
  
 3. **Record Additional Context** 3. **Record Additional Context**
    Enrich logs by attaching meaningful details as a dictionary:    Enrich logs by attaching meaningful details as a dictionary:
-   ```python+<code>    
 +   python
    audit_logger.log_event(    audit_logger.log_event(
        "Training completed",         "Training completed", 
Line 113: Line 116:
        status="INFO"        status="INFO"
    )    )
-   ```+</code>   
  
 4. **Log Failures or Anomalies** 4. **Log Failures or Anomalies**
-   Use the `status` parameter to log potential issues or failures: +   Use the **status** parameter to log potential issues or failures: 
-   ```python+    
 +<code> 
 +   python
    audit_logger.log_event(    audit_logger.log_event(
        "Pipeline execution failed",         "Pipeline execution failed", 
Line 123: Line 128:
        status="FAILURE"        status="FAILURE"
    )    )
-   ``` +</code>   
- +
---- +
 ===== 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()
  
Line 161: Line 161:
         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)
Line 179: Line 177:
     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):
Line 198: Line 197:
         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:
Line 219: Line 218:
             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 =====
Line 231: Line 229:
 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"]
Line 241: Line 240:
             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
  
Line 258: Line 256:
             "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.1748521265.txt.gz · Last modified: 2025/05/29 12:21 by eagleeyenebula