## PRD: Enhanced Workload Classification with Machine Learning

This PRD builds upon the initial workload type classification (GPM-084) by introducing machine learning to improve accuracy and adapt to evolving workload patterns.

### GPM-085: Data Collection for ML Training
**Goal:** Gather data on various workloads and their corresponding performance metrics to train a machine learning model.
**Acceptance Criteria:**
- A mechanism exists to log workload characteristics (e.g., CPU usage, memory allocation, I/O operations, network traffic) for a representative set of workloads.
- The logged data includes the workload type (e.g., CPU-intensive, I/O-intensive, Mixed) as determined by the previous workload classification system (GPM-084).
- At least 1000 distinct workload instances are logged with associated performance metrics and workload type.
- Data is stored in a format suitable for machine learning training (e.g., CSV, Parquet).
**Files:** `data_collection/workload_logger.py`, `data/workload_data.csv`
**Spawns:** Implement initial ML model training and evaluation.

### GPM-086: Initial ML Model Training and Evaluation
**Goal:** Train a machine learning model to predict workload type based on performance metrics.
**Acceptance Criteria:**
- A machine learning model (e.g., Random Forest, Support Vector Machine) is trained using the collected workload data.
- The model is evaluated using appropriate metrics (e.g., accuracy, precision, recall, F1-score) on a held-out test set (at least 20% of the data).
- Model evaluation results (metrics) are logged and accessible.
- A baseline model (e.g., majority class classifier) is implemented and its performance is compared to the trained model. The trained model must outperform the baseline.
**Files:** `ml/model_training.py`, `ml/model.pkl`, `ml/evaluation_results.txt`
**Spawns:** Integrate the trained ML model into the workload classification pipeline.

### GPM-087: ML Model Integration into Classification Pipeline
**Goal:** Replace or augment the existing rule-based workload classification with the trained ML model.
**Acceptance Criteria:**
- The trained ML model is integrated into the workload classification pipeline.
- A configuration option exists to switch between the rule-based classification (from GPM-084) and the ML-based classification.
- The ML model predicts the workload type based on real-time performance metrics.
- The output of the classification pipeline includes the predicted workload type and the confidence score from the ML model.
**Files:** `workload_classification/classifier.py`, `config/aiva.conf`
**Spawns:** Implement A/B testing to compare rule-based and ML-based classification.

### GPM-088: A/B Testing of Classification Methods
**Goal:** Evaluate the performance of the rule-based and ML-based classification methods in a production environment.
**Acceptance Criteria:**
- A/B testing infrastructure is implemented to route a portion of incoming workloads (e.g., 50%) to the ML-based classification and the remaining portion to the rule-based classification.
- Performance metrics (e.g., resource utilization, response time, error rates) are tracked for both classification methods.
- Statistical analysis is performed to compare the performance of the two classification methods.
- A report is generated summarizing the A/B testing results and recommending the best classification method.
**Files:** `ab_testing/ab_test_manager.py`, `reports/ab_test_report.md`
**Spawns:** Implement online model retraining and continuous improvement.

### GPM-089: Model Retraining and Continuous Improvement
**Goal:** Implement a mechanism to automatically retrain the ML model with new data to maintain accuracy and adapt to changing workload patterns.
**Acceptance Criteria:**
- A process is in place to periodically retrain the ML model (e.g., weekly or monthly).
- New workload data is automatically incorporated into the training dataset.
- The model is retrained using the updated dataset.
- The performance of the retrained model is evaluated.
- If the performance of the retrained model is significantly better than the current model, the current model is replaced with the retrained model.
**Files:** `ml/model_retraining.py`, `ml/model_versioning.py`
**Spawns:** Implement anomaly detection for workloads to identify unusual patterns.

### GPM-090: Logging of ML Model Predictions and Metrics
**Goal:** Enhanced logging to track ML model predictions and performance metrics for analysis and debugging.
**Acceptance Criteria:**
- Detailed logs capture the input features used by the ML model for each prediction.
- Logs include the predicted workload type, confidence score, and actual resource utilization of the workload.
- Log data is stored in a structured format (e.g., JSON) for easy querying and analysis.
- A dashboard is created to visualize key metrics, such as prediction accuracy over time and distribution of predicted workload types.
**Files:** `logging/ml_logging.py`, `dashboard/ml_dashboard.py`
**Spawns:** Explore advanced feature engineering techniques to improve model accuracy.
