feat(Production): Complete production deployment infrastructure

- Add comprehensive health check system with multiple endpoints
- Add Prometheus metrics endpoint
- Add production logging configurations (5 strategies)
- Add complete deployment documentation suite:
  * QUICKSTART.md - 30-minute deployment guide
  * DEPLOYMENT_CHECKLIST.md - Printable verification checklist
  * DEPLOYMENT_WORKFLOW.md - Complete deployment lifecycle
  * PRODUCTION_DEPLOYMENT.md - Comprehensive technical reference
  * production-logging.md - Logging configuration guide
  * ANSIBLE_DEPLOYMENT.md - Infrastructure as Code automation
  * README.md - Navigation hub
  * DEPLOYMENT_SUMMARY.md - Executive summary
- Add deployment scripts and automation
- Add DEPLOYMENT_PLAN.md - Concrete plan for immediate deployment
- Update README with production-ready features

All production infrastructure is now complete and ready for deployment.

src/Framework/Queue/MachineLearning/QueueAnomalyModelAdapter.php

@@ -0,0 +1,215 @@
+<?php
+
+declare(strict_types=1);
+
+namespace App\Framework\Queue\MachineLearning;
+
+use App\Framework\MachineLearning\ModelManagement\ModelRegistry;
+use App\Framework\MachineLearning\ModelManagement\ModelPerformanceMonitor;
+use App\Framework\MachineLearning\ModelManagement\ValueObjects\ModelMetadata;
+use App\Framework\Core\ValueObjects\Version;
+use App\Framework\Core\ValueObjects\Timestamp;
+use App\Framework\Queue\MachineLearning\ValueObjects\JobFeatures;
+use App\Framework\Queue\MachineLearning\ValueObjects\JobAnomalyResult;
+
+/**
+ * Queue Job Anomaly Detection Model Management Adapter
+ *
+ * Integrates JobAnomalyDetector with the ML Model Management System:
+ * - Automatic model registration
+ * - Real-time performance tracking
+ * - Prediction monitoring
+ * - Configuration management
+ *
+ * Usage:
+ * ```php
+ * $adapter = new QueueAnomalyModelAdapter($registry, $performanceMonitor, $detector);
+ *
+ * // Register current model version
+ * $adapter->registerCurrentModel();
+ *
+ * // Analyze with tracking
+ * $result = $adapter->analyzeWithTracking($features, $groundTruth);
+ * ```
+ */
+final readonly class QueueAnomalyModelAdapter
+{
+    private const MODEL_NAME = 'queue-anomaly';
+    private const CURRENT_VERSION = '1.0.0';
+
+    public function __construct(
+        private ModelRegistry $registry,
+        private ModelPerformanceMonitor $performanceMonitor,
+        private JobAnomalyDetector $detector
+    ) {}
+
+    /**
+     * Register current queue anomaly model in registry
+     */
+    public function registerCurrentModel(?array $performanceMetrics = null): ModelMetadata
+    {
+        $version = Version::fromString(self::CURRENT_VERSION);
+
+        // Check if already registered
+        if ($this->registry->exists(self::MODEL_NAME, $version)) {
+            return $this->registry->get(self::MODEL_NAME, $version);
+        }
+
+        // Create metadata
+        $metadata = ModelMetadata::forQueueAnomaly(
+            version: $version,
+            configuration: $this->detector->getConfiguration()
+        );
+
+        // Add performance metrics if provided
+        if ($performanceMetrics !== null) {
+            $metadata = $metadata->withPerformanceMetrics($performanceMetrics);
+        }
+
+        // Register in registry
+        $this->registry->register($metadata);
+
+        return $metadata;
+    }
+
+    /**
+     * Analyze job features with automatic performance tracking
+     *
+     * @param JobFeatures $features Job execution features
+     * @param bool|null $groundTruth Ground truth (if known) - true if job is anomalous
+     *
+     * @return array Analysis result with tracking info
+     */
+    public function analyzeWithTracking(
+        JobFeatures $features,
+        ?bool $groundTruth = null
+    ): array {
+        // Perform ML analysis
+        $analysisResult = $this->detector->detect($features);
+
+        // Determine prediction
+        $prediction = $analysisResult->isAnomalous;
+        $confidence = $analysisResult->anomalyScore->getValue() / 100.0; // Convert 0-100 to 0.0-1.0
+
+        // Track prediction in performance monitor
+        $this->performanceMonitor->trackPrediction(
+            modelName: self::MODEL_NAME,
+            version: Version::fromString(self::CURRENT_VERSION),
+            prediction: $prediction,
+            actual: $groundTruth,
+            confidence: $confidence,
+            features: $this->extractFeatureSummary($analysisResult)
+        );
+
+        // Convert result to array format
+        $resultArray = [
+            'is_anomalous' => $analysisResult->isAnomalous,
+            'anomaly_score' => $analysisResult->anomalyScore->getValue(),
+            'feature_scores' => array_map(
+                fn($score) => $score->getValue(),
+                $analysisResult->featureScores
+            ),
+            'detected_patterns' => $analysisResult->detectedPatterns,
+            'primary_indicator' => $analysisResult->primaryIndicator,
+            'success' => true
+        ];
+
+        // Add tracking info
+        $resultArray['tracking'] = [
+            'model_name' => self::MODEL_NAME,
+            'model_version' => self::CURRENT_VERSION,
+            'prediction' => $prediction ? 'anomalous' : 'normal',
+            'ground_truth' => $groundTruth,
+            'tracked' => true,
+        ];
+
+        return $resultArray;
+    }
+
+    /**
+     * Get current model performance metrics
+     */
+    public function getCurrentPerformanceMetrics(): array
+    {
+        return $this->performanceMonitor->getCurrentMetrics(
+            self::MODEL_NAME,
+            Version::fromString(self::CURRENT_VERSION)
+        );
+    }
+
+    /**
+     * Check if model performance has degraded
+     */
+    public function checkPerformanceDegradation(float $thresholdPercent = 0.05): array
+    {
+        return $this->performanceMonitor->getPerformanceDegradationInfo(
+            self::MODEL_NAME,
+            Version::fromString(self::CURRENT_VERSION),
+            $thresholdPercent
+        );
+    }
+
+    /**
+     * Update model configuration in registry
+     */
+    public function updateConfiguration(array $newConfiguration): void
+    {
+        $version = Version::fromString(self::CURRENT_VERSION);
+        $metadata = $this->registry->get(self::MODEL_NAME, $version);
+
+        if ($metadata === null) {
+            throw new \RuntimeException(
+                'Model not registered. Call registerCurrentModel() first.'
+            );
+        }
+
+        $updated = $metadata->withConfiguration($newConfiguration);
+        $this->registry->update($updated);
+    }
+
+    /**
+     * Deploy current model to production
+     */
+    public function deployToProduction(): void
+    {
+        $version = Version::fromString(self::CURRENT_VERSION);
+        $metadata = $this->registry->get(self::MODEL_NAME, $version);
+
+        if ($metadata === null) {
+            throw new \RuntimeException(
+                'Model not registered. Call registerCurrentModel() first.'
+            );
+        }
+
+        $deployed = $metadata->withDeployment(
+            environment: 'production',
+            deployedAt: Timestamp::now()
+        );
+
+        $this->registry->update($deployed);
+    }
+
+    /**
+     * Get model metadata
+     */
+    public function getModelMetadata(): ?ModelMetadata
+    {
+        return $this->registry->get(
+            self::MODEL_NAME,
+            Version::fromString(self::CURRENT_VERSION)
+        );
+    }
+
+    /**
+     * Extract feature summary for tracking
+     */
+    private function extractFeatureSummary(JobAnomalyResult $result): array
+    {
+        return [
+            'feature_count' => count($result->featureScores),
+            'pattern_count' => count($result->detectedPatterns),
+            'primary_indicator' => $result->primaryIndicator,
+            'is_anomalous' => $result->isAnomalous,
+        ];
+    }
+}