feat(Deployment): Integrate Ansible deployment via PHP deployment pipeline

- Create AnsibleDeployStage using framework's Process module for secure command execution
- Integrate AnsibleDeployStage into DeploymentPipelineCommands for production deployments
- Add force_deploy flag support in Ansible playbook to override stale locks
- Use PHP deployment module as orchestrator (php console.php deploy:production)
- Fix ErrorAggregationInitializer to use Environment class instead of $_ENV superglobal

Architecture:
- BuildStage → AnsibleDeployStage → HealthCheckStage for production
- Process module provides timeout, error handling, and output capture
- Ansible playbook supports rollback via rollback-git-based.yml
- Zero-downtime deployments with health checks

src/Framework/MachineLearning/Migrations/CreateMlConfidenceBaselinesTable.php

@@ -0,0 +1,68 @@
+<?php
+
+declare(strict_types=1);
+
+namespace App\Framework\MachineLearning\Migrations;
+
+use App\Framework\Database\ConnectionInterface;
+use App\Framework\Database\Migration\Migration;
+use App\Framework\Database\Migration\MigrationVersion;
+use App\Framework\Database\Schema\Blueprint;
+use App\Framework\Database\Schema\Schema;
+
+/**
+ * Create ML Confidence Baselines Table
+ *
+ * Stores historical confidence statistics for drift detection:
+ * - Average confidence per model version
+ * - Standard deviation for anomaly detection
+ * - Last update timestamp
+ *
+ * Uses ON CONFLICT for upsert pattern - baselines are updated
+ * as new predictions come in.
+ */
+final class CreateMlConfidenceBaselinesTable implements Migration
+{
+    public function up(ConnectionInterface $connection): void
+    {
+        $schema = new Schema($connection);
+
+        $schema->createIfNotExists('ml_confidence_baselines', function (Blueprint $table) {
+            // Model identification
+            $table->string('model_name', 255);
+            $table->string('version', 50);
+
+            // Confidence statistics
+            $table->decimal('avg_confidence', 5, 4); // Average confidence score
+            $table->decimal('std_dev_confidence', 5, 4); // Standard deviation
+
+            // Tracking
+            $table->timestamp('updated_at')->useCurrent();
+
+            // Primary key: model_name + version (one baseline per model version)
+            $table->primary('model_name', 'version');
+
+            // Index for lookup by model
+            $table->index(['model_name'], 'idx_ml_baselines_model');
+        });
+
+        $schema->execute();
+    }
+
+    public function down(ConnectionInterface $connection): void
+    {
+        $schema = new Schema($connection);
+        $schema->dropIfExists('ml_confidence_baselines');
+        $schema->execute();
+    }
+
+    public function getVersion(): MigrationVersion
+    {
+        return MigrationVersion::fromTimestamp("2025_01_26_100002");
+    }
+
+    public function getDescription(): string
+    {
+        return "Create ML confidence baselines table for drift detection";
+    }
+}

src/Framework/MachineLearning/Migrations/CreateMlModelsTable.php

@@ -0,0 +1,76 @@
+<?php
+
+declare(strict_types=1);
+
+namespace App\Framework\MachineLearning\Migrations;
+
+use App\Framework\Database\ConnectionInterface;
+use App\Framework\Database\Migration\Migration;
+use App\Framework\Database\Migration\MigrationVersion;
+use App\Framework\Database\Schema\Blueprint;
+use App\Framework\Database\Schema\Schema;
+
+/**
+ * Create ML Models Table
+ *
+ * Stores machine learning model metadata including:
+ * - Model identification (name, version)
+ * - Model type (supervised, unsupervised, reinforcement)
+ * - Configuration and performance metrics (JSON)
+ * - Deployment status and environment
+ */
+final class CreateMlModelsTable implements Migration
+{
+    public function up(ConnectionInterface $connection): void
+    {
+        $schema = new Schema($connection);
+
+        $schema->createIfNotExists('ml_models', function (Blueprint $table) {
+            // Primary identification
+            $table->string('model_name', 255);
+            $table->string('version', 50);
+
+            // Model metadata
+            $table->string('model_type', 50); // supervised, unsupervised, reinforcement
+            $table->text('configuration'); // JSON: hyperparameters, architecture, etc.
+            $table->text('performance_metrics'); // JSON: accuracy, precision, recall, etc.
+
+            // Deployment tracking
+            $table->boolean('is_deployed')->default(false);
+            $table->string('environment', 50); // development, staging, production
+
+            // Documentation
+            $table->text('description')->nullable();
+
+            // Timestamps
+            $table->timestamp('created_at')->useCurrent();
+
+            // Primary key: model_name + version
+            $table->primary('model_name', 'version');
+
+            // Indexes for efficient queries
+            $table->index(['model_type'], 'idx_ml_models_type');
+            $table->index(['environment', 'is_deployed'], 'idx_ml_models_env');
+            $table->index(['created_at'], 'idx_ml_models_created');
+        });
+
+        $schema->execute();
+    }
+
+    public function down(ConnectionInterface $connection): void
+    {
+        $schema = new Schema($connection);
+        $schema->dropIfExists('ml_models');
+        $schema->execute();
+    }
+
+    public function getVersion(): MigrationVersion
+    {
+        return MigrationVersion::fromTimestamp("2025_01_26_100000");
+    }
+
+    public function getDescription(): string
+    {
+        return "Create ML models metadata table";
+    }
+}

src/Framework/MachineLearning/Migrations/CreateMlPredictionsTable.php

@@ -0,0 +1,77 @@
+<?php
+
+declare(strict_types=1);
+
+namespace App\Framework\MachineLearning\Migrations;
+
+use App\Framework\Database\ConnectionInterface;
+use App\Framework\Database\Migration\Migration;
+use App\Framework\Database\Migration\MigrationVersion;
+use App\Framework\Database\Schema\Blueprint;
+use App\Framework\Database\Schema\Schema;
+
+/**
+ * Create ML Predictions Table
+ *
+ * Stores individual prediction records for performance tracking:
+ * - Prediction inputs and outputs
+ * - Actual outcomes (when available)
+ * - Confidence scores
+ * - Correctness evaluation
+ *
+ * Performance optimizations:
+ * - Composite index on (model_name, version, timestamp) for time-window queries
+ * - Partitioning-ready for large-scale deployments
+ */
+final class CreateMlPredictionsTable implements Migration
+{
+    public function up(ConnectionInterface $connection): void
+    {
+        $schema = new Schema($connection);
+
+        $schema->createIfNotExists('ml_predictions', function (Blueprint $table) {
+            $table->id(); // Auto-incrementing primary key
+
+            // Model identification
+            $table->string('model_name', 255);
+            $table->string('version', 50);
+
+            // Prediction data (JSON)
+            $table->text('prediction'); // JSON: model output
+            $table->text('actual'); // JSON: actual outcome (when available)
+            $table->text('features'); // JSON: input features
+
+            // Performance metrics
+            $table->decimal('confidence', 5, 4); // 0.0000 to 1.0000
+            $table->boolean('is_correct')->nullable(); // null until actual outcome known
+
+            // Timing
+            $table->timestamp('timestamp')->useCurrent();
+
+            // Composite index for efficient time-window queries
+            $table->index(['model_name', 'version', 'timestamp'], 'idx_ml_predictions_lookup');
+
+            // Index for cleanup operations
+            $table->index(['timestamp'], 'idx_ml_predictions_timestamp');
+        });
+
+        $schema->execute();
+    }
+
+    public function down(ConnectionInterface $connection): void
+    {
+        $schema = new Schema($connection);
+        $schema->dropIfExists('ml_predictions');
+        $schema->execute();
+    }
+
+    public function getVersion(): MigrationVersion
+    {
+        return MigrationVersion::fromTimestamp("2025_01_26_100001");
+    }
+
+    public function getDescription(): string
+    {
+        return "Create ML predictions tracking table";
+    }
+}