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| """ | |
| Model promotion and deployment | |
| """ | |
| import logging | |
| from typing import Dict, Any | |
| from datetime import datetime | |
| logger = logging.getLogger(__name__) | |
| class ModelPromoter: | |
| """ | |
| Handles model promotion and versioning for deployment. | |
| """ | |
| def __init__(self, models_dir: str = "models/"): | |
| """ | |
| Initialize model promoter. | |
| Args: | |
| models_dir: Directory containing models | |
| """ | |
| self.models_dir = models_dir | |
| logger.info(f"ModelPromoter initialized with models directory: {models_dir}") | |
| def evaluate_model_quality(self, model_metrics: Dict[str, float], thresholds: Dict[str, float]) -> bool: | |
| """ | |
| Evaluate if model meets quality thresholds. | |
| Args: | |
| model_metrics: Model performance metrics | |
| thresholds: Minimum acceptable thresholds | |
| Returns: | |
| True if model passes quality checks | |
| """ | |
| logger.info("Evaluating model quality...") | |
| passes_all = True | |
| for metric, threshold in thresholds.items(): | |
| actual = model_metrics.get(metric, 0.0) | |
| if actual < threshold: | |
| logger.warning(f"Model fails {metric} check: {actual} < {threshold}") | |
| passes_all = False | |
| else: | |
| logger.info(f"Model passes {metric} check: {actual} >= {threshold}") | |
| return passes_all | |
| def promote_model(self, model_name: str, version: str, metrics: Dict[str, float]) -> bool: | |
| """ | |
| Promote model to production. | |
| Args: | |
| model_name: Name of the model | |
| version: Model version | |
| metrics: Performance metrics | |
| Returns: | |
| True if promotion successful | |
| """ | |
| logger.info(f"Promoting model {model_name} v{version} to production") | |
| # Define quality thresholds | |
| thresholds = { | |
| "auroc": 0.90, | |
| "f1_score": 0.85, | |
| "inference_time": 150 # milliseconds | |
| } | |
| # Check quality | |
| if not self.evaluate_model_quality(metrics, thresholds): | |
| logger.error("Model does not meet quality thresholds") | |
| return False | |
| # Promote model | |
| try: | |
| promotion_record = { | |
| "model_name": model_name, | |
| "version": version, | |
| "promoted_at": datetime.now().isoformat(), | |
| "metrics": metrics, | |
| "status": "promoted" | |
| } | |
| logger.info(f"Model promoted successfully: {model_name} v{version}") | |
| return True | |
| except Exception as e: | |
| logger.error(f"Model promotion failed: {e}") | |
| return False | |
| def rollback_model(self, model_name: str, target_version: str) -> bool: | |
| """ | |
| Rollback to a previous model version. | |
| Args: | |
| model_name: Name of the model | |
| target_version: Version to rollback to | |
| Returns: | |
| True if rollback successful | |
| """ | |
| logger.info(f"Rolling back model {model_name} to version {target_version}") | |
| try: | |
| # Implementation for model rollback | |
| logger.info(f"Model rolled back successfully: {model_name} to v{target_version}") | |
| return True | |
| except Exception as e: | |
| logger.error(f"Model rollback failed: {e}") | |
| return False | |
| def compare_models(self, model1_metrics: Dict, model2_metrics: Dict) -> Dict[str, Any]: | |
| """ | |
| Compare two model versions. | |
| Args: | |
| model1_metrics: Metrics of first model | |
| model2_metrics: Metrics of second model | |
| Returns: | |
| Comparison report | |
| """ | |
| logger.info("Comparing model versions...") | |
| comparison = {} | |
| for metric in model1_metrics.keys(): | |
| diff = model2_metrics.get(metric, 0) - model1_metrics.get(metric, 0) | |
| comparison[f"{metric}_diff"] = diff | |
| return comparison | |