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ai_orchestrator [2025/05/28 20:36] – [Usage Examples] eagleeyenebulaai_orchestrator [2025/05/28 20:43] (current) – [Best Practices] eagleeyenebula
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 Execute a basic pipeline using a predefined configuration. Execute a basic pipeline using a predefined configuration.
  
-```python+<code> 
 +python
 from ai_orchestrator import AIOrchestrator from ai_orchestrator import AIOrchestrator
- +</code> 
-# Configuration for the orchestrator+**Configuration for the orchestrator** 
 +<code>
 config = { config = {
     "training_data_path": "data/train_data.csv",     "training_data_path": "data/train_data.csv",
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     "training_data": {"feature1": [0.1, 0.2], "label": [0, 1]},     "training_data": {"feature1": [0.1, 0.2], "label": [0, 1]},
 } }
- +</code> 
-# Initialize the orchestrator+**Initialize the orchestrator** 
 +<code>
 orchestrator = AIOrchestrator(config) orchestrator = AIOrchestrator(config)
- +</code> 
-# Run the AI pipeline workflow+**Run the AI pipeline workflow** 
 +<code>
 orchestrator.execute_pipeline() orchestrator.execute_pipeline()
-```+</code>
  
 **Explanation**:   **Explanation**:  
-- Loads configuration, integrates feedback, detects drift, retrains the model, and creates a PDF report summarizing pipeline execution. +   * Loads configuration, integrates feedback, detects drift, retrains the model, and creates a PDF report summarizing pipeline execution.
- +
---- +
 ==== Example 2: Handling Feedback Integration ==== ==== Example 2: Handling Feedback Integration ====
  
 Integrate external user feedback into the training pipeline. Integrate external user feedback into the training pipeline.
- +<code> 
-```python+python
 from ai_orchestrator import AIOrchestrator from ai_orchestrator import AIOrchestrator
  
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 orchestrator = AIOrchestrator(config) orchestrator = AIOrchestrator(config)
- +</code> 
-# Integrate only the feedback loop+**Integrate only the feedback loop** 
 +<code>
 FeedbackLoop.integrate_feedback(config["feedback_data"], config["training_data_path"]) FeedbackLoop.integrate_feedback(config["feedback_data"], config["training_data_path"])
 print("Feedback integrated successfully!") print("Feedback integrated successfully!")
-```+</code>
  
 **Details**:   **Details**:  
-- This use case demonstrates direct feedback loop integration using the `FeedbackLoop` API. +   * This use case demonstrates direct feedback loop integration using the **FeedbackLoop** API.
- +
---- +
 ==== Example 3: Detecting and Logging Model Drift ==== ==== Example 3: Detecting and Logging Model Drift ====
  
 Use the drift-detection module to identify performance degradation. Use the drift-detection module to identify performance degradation.
- +<code> 
-```python+python
 from ai_orchestrator import AIOrchestrator from ai_orchestrator import AIOrchestrator
  
 new_data = [{"value": 0.5}, {"value": 0.7}, {"value": 0.6}] new_data = [{"value": 0.5}, {"value": 0.7}, {"value": 0.6}]
 reference_data = {"label": [0, 1, 0]} reference_data = {"label": [0, 1, 0]}
- +</code> 
-# Detect drift+**Detect drift** 
 +<code>
 drift_detected = ModelDriftMonitoring.detect_drift( drift_detected = ModelDriftMonitoring.detect_drift(
     new_data=[d["value"] for d in new_data],     new_data=[d["value"] for d in new_data],
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 print(f"Model Drift Detected: {drift_detected}") print(f"Model Drift Detected: {drift_detected}")
-``` +</code>
- +
-**Output**:   +
-`Model Drift Detected: True`+
  
 +**Output**: 
 +<code> 
 +Model Drift Detected: True
 +</code>
 **Explanation**:   **Explanation**:  
-- This simple drift-detection function compares `new_data` against the `reference_data` to determine if the model performance deviates significantly. +   * This simple drift-detection function compares **new_data** against the **reference_data** to determine if the model performance deviates significantly.
- +
---- +
 ==== Example 4: Automated Retraining ==== ==== Example 4: Automated Retraining ====
  
 Trigger automated retraining when drift is detected. Trigger automated retraining when drift is detected.
- +<code> 
-```python+python
 from ai_orchestrator import AIOrchestrator from ai_orchestrator import AIOrchestrator
  
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     "deployment_path": "deployment/new_model",     "deployment_path": "deployment/new_model",
 } }
- +</code> 
-# Simulated drift+**Simulated drift** 
 +<code>
 drift_detected = True drift_detected = True
 if drift_detected: if drift_detected:
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         config["deployment_path"]         config["deployment_path"]
     )     )
-```+</code>
  
 **Explanation**:   **Explanation**:  
-- Simulates detecting drift and triggers the model retraining workflow with a specified training dataset and deployment directory. +   * Simulates detecting drift and triggers the model retraining workflow with a specified training dataset and deployment directory.
- +
---- +
 ==== Example 5: Generating Advanced Reports ==== ==== Example 5: Generating Advanced Reports ====
  
 Generate a detailed PDF report summarizing algorithm performance. Generate a detailed PDF report summarizing algorithm performance.
- +<code> 
-```python+python
 from ai_advanced_reporting import AdvancedReporting from ai_advanced_reporting import AdvancedReporting
- +</code> 
-# Report data+**Report data** 
 +<code>
 pipeline_metrics = { pipeline_metrics = {
     "Accuracy": 92,     "Accuracy": 92,
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     "Drift Detected": False,     "Drift Detected": False,
 } }
- +</code> 
-# Generate report+**Generate report** 
 +<code>
 AdvancedReporting.generate_pdf_report( AdvancedReporting.generate_pdf_report(
     pipeline_metrics,     pipeline_metrics,
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 ) )
 print("Report generated successfully.") print("Report generated successfully.")
-```+</code>
  
 **Explanation**:   **Explanation**:  
-- Produces an advanced report in PDF format, summarizing metrics like accuracy, precision, and model drift status for transparent reporting. +   * Produces an advanced report in PDF format, summarizing metrics like accuracy, precision, and model drift status for transparent reporting.
- +
---- +
 ===== Advanced Features ===== ===== Advanced Features =====
  
 1. **Dynamic Configurations**:   1. **Dynamic Configurations**:  
-   Load configurations dynamically via JSON or YAML files for flexible and modular pipeline setups.+   * Load configurations dynamically via **JSON** or **YAML** files for flexible and modular pipeline setups.
  
 2. **Feedback Quality Control**:   2. **Feedback Quality Control**:  
-   Implement filters to sanitize and validate feedback data before integration.+   * Implement filters to sanitize and validate feedback data before integration.
  
 3. **Real-Time Drift Alerts**:   3. **Real-Time Drift Alerts**:  
-   Use real-time monitoring to trigger alerts immediately upon drift detection.+   * Use real-time monitoring to trigger alerts immediately upon drift detection.
  
 4. **Error Retry Mechanism**:   4. **Error Retry Mechanism**:  
-   Introduce retry logic to handle transient pipeline failures gracefully.+   * Introduce retry logic to handle transient pipeline failures gracefully.
  
 5. **Interactive Visualizations**:   5. **Interactive Visualizations**:  
-   Extend reporting functionalities to generate charts or graphical summaries alongside PDF reports. +   * Extend reporting functionalities to generate charts or graphical summaries alongside PDF reports.
- +
---- +
 ===== Extensibility ===== ===== Extensibility =====
  
 1. **Custom Feedback Handlers**:   1. **Custom Feedback Handlers**:  
-   Write extensions for domain-specific feedback loops or annotation pipelines.+   * Write extensions for domain-specific feedback loops or annotation pipelines.
  
 2. **Model Deployment Validators**:   2. **Model Deployment Validators**:  
-   Add validation routines to ensure retrained models meet production quality standards.+   * Add validation routines to ensure retrained models meet production quality standards.
  
 3. **Hybrid Model Support**:   3. **Hybrid Model Support**:  
-   Enable workflows that support hybrid models (e.g., combining ML and rule-based systems).+   * Enable workflows that support hybrid models (e.g., combining ML and rule-based systems).
  
 4. **Cloud Integration**:   4. **Cloud Integration**:  
-   Extend the `AIOrchestrator` to work with cloud platforms like AWS Sagemaker, Azure ML, or GCP AI. +   * Extend the `AIOrchestrator` to work with cloud platforms like AWS Sagemaker, Azure ML, or GCP AI.
- +
---- +
 ===== Best Practices ===== ===== Best Practices =====
  
-- **Monitor Drift Regularly**:   + **Monitor Drift Regularly**:   
-  Schedule routine model drift checks using cron jobs or pipeline automation tools. +  * Schedule routine model drift checks using cron jobs or pipeline automation tools.
- +
-- **Validate Feedback Data**:   +
-  Ensure that feedback data is clean, labeled accurately, and suitable for training before integration. +
- +
-- **Leverage Modular Components**:   +
-  Use each module (feedback, retraining, reporting) separately as needed to ensure scalability and maintainability.+
  
-- **Secure Data**:   + **Validate Feedback Data**:   
-  Protect training datasets, feedback records, and reports from unauthorized access.+  * Ensure that feedback data is clean, labeled accurately, and suitable for training before integration.
  
-- **Log Everything**:   + **Leverage Modular Components**:   
-  Maintain comprehensive logs for the entire pipeline to aid in debugging and compliance.+  * Use each module (feedback, retraining, reporting) separately as needed to ensure scalability and maintainability.
  
----+ **Secure Data**:   
 +  * Protect training datasets, feedback records, and reports from unauthorized access.
  
 + **Log Everything**:  
 +  * Maintain comprehensive logs for the entire pipeline to aid in debugging and compliance.
 ===== Conclusion ===== ===== Conclusion =====
  
ai_orchestrator.1748464564.txt.gz · Last modified: 2025/05/28 20:36 by eagleeyenebula