Technical
Data & Knowledge Layer
ML/AI Integration

ML/AI Integration

ML/AI Integration with Supabase

The ML/AI Integration component leverages Supabase as the unified data platform for machine learning workflows, providing feature stores, model metadata management, real-time training data, and vector-based ML operations. Supabase’s PostgreSQL foundation with pgvector enables sophisticated AI/ML pipelines with ACID guarantees and real-time capabilities.

Architecture Overview 

  graph TB
    A[Supabase Data Platform] --> B[Feature Engineering]
    B --> C[Model Development]
    C --> D[Training Pipeline]
    D --> E[Model Registry]
    E --> F[Model Deployment]
    F --> G[Model Serving]
    G --> H[Real-time Monitoring]
    H --> A
    
    subgraph "Supabase Feature Layer"
        B --> B1[PostgreSQL Feature Store]
        B --> B2[JSONB Feature Validation]
        B --> B3[Feature Lineage Tables]
        B --> B4[Real-time Feature Updates]
    end
    
    subgraph "Supabase Training Layer"
        D --> D1[Training Data Management]
        D --> D2[Hyperparameter Storage]
        D --> D3[Experiment Metadata]
        D --> D4[Model Validation Results]
    end
    
    subgraph "Supabase Deployment Layer"
        F --> F1[A/B Test Configuration]
        F --> F2[Deployment Metadata]
        F --> F3[Model Versioning]
        F --> F4[Serving Statistics]
    end
    
    subgraph "Supabase Feedback Layer"
        H --> H1[Performance Metrics (TimescaleDB)]
        H --> H2[Drift Detection (pgvector)]
        H --> H3[Feedback Storage]
        H --> H4[Continual Learning Data]
    end

Supabase Feature Store Implementation

PostgreSQL-Based Feature Engineering 

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from dataclasses import dataclass
from typing import Dict, List, Any, Optional, Union, Callable
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import asyncio
from feast import FeatureStore, Entity, Feature, FeatureView, ValueType
from feast.data_source import FileSource

@dataclass
class FeatureDefinition:
    name: str
    description: str
    value_type: str
    transformation: Optional[Callable] = None
    dependencies: List[str] = None
    update_frequency: str = "daily"
    validation_rules: List[Dict] = None

class AIMatrixFeatureStore:
    def __init__(self, config_path: str):
        self.feast_store = FeatureStore(repo_path=config_path)
        self.feature_registry = {}
        self.transformation_engine = FeatureTransformationEngine()
        self.validation_engine = FeatureValidationEngine()
        self.lineage_tracker = FeatureLineageTracker()
        
    async def register_feature_set(self, feature_definitions: List[FeatureDefinition],
                                 entity_name: str) -> str:
        """Register a new feature set with comprehensive metadata"""
        
        # Create Feast entity
        entity = Entity(
            name=entity_name,
            value_type=ValueType.STRING,
            description=f"Entity for {entity_name} features"
        )
        
        # Create features
        features = []
        for feature_def in feature_definitions:
            feature = Feature(
                name=feature_def.name,
                dtype=self._map_value_type(feature_def.value_type),
                description=feature_def.description
            )
            features.append(feature)
            
            # Register in internal registry
            self.feature_registry[feature_def.name] = feature_def
            
        # Create feature view
        feature_view = FeatureView(
            name=f"{entity_name}_features",
            entities=[entity_name],
            features=features,
            ttl=timedelta(days=1),
            source=FileSource(
                path="data/feature_data.parquet",
                timestamp_field="timestamp"
            )
        )
        
        # Apply to feature store
        self.feast_store.apply([entity, feature_view])
        
        # Track lineage
        await self.lineage_tracker.register_feature_set(
            entity_name, feature_definitions
        )
        
        return f"{entity_name}_features"
        
    async def compute_features(self, entity_ids: List[str], 
                             feature_names: List[str],
                             timestamp: Optional[datetime] = None) -> pd.DataFrame:
        """Compute features with real-time transformations"""
        
        if timestamp is None:
            timestamp = datetime.utcnow()
            
        # Get base features from store
        feature_vector = self.feast_store.get_online_features(
            features=feature_names,
            entity_rows=[{"entity_id": entity_id} for entity_id in entity_ids]
        )
        
        base_df = feature_vector.to_df()
        
        # Apply real-time transformations
        transformed_df = await self.transformation_engine.apply_transformations(
            base_df, feature_names, timestamp
        )
        
        # Validate features
        validation_results = await self.validation_engine.validate_features(
            transformed_df, feature_names
        )
        
        # Log validation issues
        if not all(validation_results.values()):
            await self._handle_validation_failures(validation_results)
            
        return transformed_df
        
    async def real_time_feature_pipeline(self, stream_config: Dict) -> None:
        """Set up real-time feature computation pipeline"""
        
        from kafka import KafkaConsumer
        import json
        
        consumer = KafkaConsumer(
            stream_config['topic'],
            bootstrap_servers=stream_config['bootstrap_servers'],
            value_deserializer=lambda m: json.loads(m.decode('utf-8'))
        )
        
        feature_batch = []
        batch_size = stream_config.get('batch_size', 100)
        
        async for message in consumer:
            event_data = message.value
            
            # Extract features from event
            features = await self._extract_real_time_features(event_data)
            feature_batch.append(features)
            
            # Process batch when full
            if len(feature_batch) >= batch_size:
                await self._process_feature_batch(feature_batch)
                feature_batch = []
                
    async def _extract_real_time_features(self, event_data: Dict) -> Dict:
        """Extract features from real-time event data"""
        
        features = {
            'entity_id': event_data['entity_id'],
            'timestamp': datetime.utcnow()
        }
        
        # Time-based features
        now = datetime.utcnow()
        features.update({
            'hour_of_day': now.hour,
            'day_of_week': now.weekday(),
            'is_weekend': now.weekday() >= 5,
            'is_business_hours': 9 <= now.hour <= 17
        })
        
        # Event-specific features
        if 'user_action' in event_data:
            features.update({
                'action_type': event_data['user_action']['type'],
                'action_value': event_data['user_action'].get('value', 0),
                'session_duration': event_data.get('session_duration', 0)
            })
            
        # Aggregation features (from recent history)
        historical_features = await self._compute_aggregation_features(
            event_data['entity_id'], now
        )
        features.update(historical_features)
        
        return features
        
    async def _compute_aggregation_features(self, entity_id: str, 
                                          timestamp: datetime) -> Dict:
        """Compute time-window aggregation features"""
        
        # Define time windows
        time_windows = {
            '1h': timedelta(hours=1),
            '24h': timedelta(days=1),
            '7d': timedelta(days=7),
            '30d': timedelta(days=30)
        }
        
        aggregation_features = {}
        
        for window_name, window_size in time_windows.items():
            start_time = timestamp - window_size
            
            # Query historical data for the window
            historical_data = await self._query_historical_events(
                entity_id, start_time, timestamp
            )
            
            if historical_data:
                # Compute aggregations
                aggregation_features.update({
                    f'count_{window_name}': len(historical_data),
                    f'sum_value_{window_name}': sum(
                        event.get('value', 0) for event in historical_data
                    ),
                    f'avg_value_{window_name}': np.mean([
                        event.get('value', 0) for event in historical_data
                    ]),
                    f'unique_actions_{window_name}': len(set(
                        event.get('action_type') for event in historical_data
                    ))
                })
                
        return aggregation_features

class FeatureTransformationEngine:
    def __init__(self):
        self.transformations = {
            'log': self._log_transform,
            'normalize': self._normalize,
            'binning': self._binning,
            'embedding': self._embedding_transform,
            'polynomial': self._polynomial_features,
            'interaction': self._interaction_features
        }
        
    async def apply_transformations(self, df: pd.DataFrame, 
                                  feature_names: List[str],
                                  timestamp: datetime) -> pd.DataFrame:
        """Apply registered transformations to features"""
        
        transformed_df = df.copy()
        
        for feature_name in feature_names:
            if feature_name in self.feature_registry:
                feature_def = self.feature_registry[feature_name]
                
                if feature_def.transformation:
                    transformation_config = feature_def.transformation
                    transform_func = self.transformations[transformation_config['type']]
                    
                    transformed_df[feature_name] = await transform_func(
                        transformed_df[feature_name],
                        transformation_config.get('params', {})
                    )
                    
        # Apply cross-feature transformations
        transformed_df = await self._apply_cross_feature_transformations(
            transformed_df, timestamp
        )
        
        return transformed_df
        
    async def _log_transform(self, series: pd.Series, params: Dict) -> pd.Series:
        """Apply logarithmic transformation"""
        offset = params.get('offset', 1)
        return np.log(series + offset)
        
    async def _normalize(self, series: pd.Series, params: Dict) -> pd.Series:
        """Normalize features using specified method"""
        method = params.get('method', 'z_score')
        
        if method == 'z_score':
            return (series - series.mean()) / series.std()
        elif method == 'min_max':
            return (series - series.min()) / (series.max() - series.min())
        else:
            return series
            
    async def _embedding_transform(self, series: pd.Series, params: Dict) -> pd.Series:
        """Transform categorical features to embeddings"""
        
        # This would typically use a pre-trained embedding model
        embedding_model = params.get('model')
        embedding_dim = params.get('dimension', 50)
        
        # Placeholder for actual embedding logic
        # In practice, you'd load embedding vectors for each category
        unique_values = series.unique()
        embedding_map = {}
        
        for value in unique_values:
            # Generate or load embedding for this value
            embedding_map[value] = np.random.randn(embedding_dim)  # Placeholder
            
        # Return embedded representation
        return series.map(embedding_map)

Model Development & Training 

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import mlflow
import mlflow.sklearn
import mlflow.pytorch
from mlflow.tracking import MlflowClient
from typing import Dict, Any, Optional, List, Tuple
import optuna
from optuna.integration.mlflow import MLflowCallback
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset
import lightning as pl
from lightning.pytorch.loggers import MLFlowLogger

class AIMatrixMLPipeline:
    def __init__(self, experiment_name: str, mlflow_tracking_uri: str):
        mlflow.set_tracking_uri(mlflow_tracking_uri)
        mlflow.set_experiment(experiment_name)
        self.experiment_name = experiment_name
        self.client = MlflowClient()
        
        # Initialize training components
        self.hyperparameter_optimizer = HyperparameterOptimizer()
        self.distributed_trainer = DistributedTrainer()
        self.model_validator = ModelValidator()
        self.continual_learner = ContinualLearner()
        
    async def train_model(self, training_config: Dict[str, Any]) -> str:
        """Complete model training pipeline with MLOps integration"""
        
        with mlflow.start_run(run_name=training_config.get('run_name')) as run:
            # Log configuration
            mlflow.log_params(training_config)
            
            # Load and prepare data
            train_data, val_data, test_data = await self._prepare_data(
                training_config['data_config']
            )
            
            # Hyperparameter optimization
            best_params = None
            if training_config.get('optimize_hyperparameters', False):
                best_params = await self.hyperparameter_optimizer.optimize(
                    training_config, train_data, val_data
                )
                mlflow.log_params(best_params)
                training_config.update(best_params)
                
            # Model training
            model = await self._create_model(training_config['model_config'])
            
            if training_config.get('distributed_training', False):
                trained_model = await self.distributed_trainer.train(
                    model, train_data, val_data, training_config
                )
            else:
                trained_model = await self._train_single_node(
                    model, train_data, val_data, training_config
                )
                
            # Model validation
            validation_results = await self.model_validator.validate(
                trained_model, test_data, training_config
            )
            
            # Log validation metrics
            for metric_name, metric_value in validation_results.items():
                mlflow.log_metric(metric_name, metric_value)
                
            # Model registration
            if validation_results['performance_score'] > training_config.get('min_performance', 0.8):
                model_uri = await self._register_model(
                    trained_model, training_config, validation_results
                )
                
                return model_uri
            else:
                raise ValueError(f"Model performance below threshold: {validation_results['performance_score']}")
                
    async def _create_model(self, model_config: Dict) -> nn.Module:
        """Create model based on configuration"""
        
        model_type = model_config['type']
        
        if model_type == 'transformer':
            return TransformerModel(model_config)
        elif model_type == 'cnn':
            return CNNModel(model_config)
        elif model_type == 'rnn':
            return RNNModel(model_config)
        elif model_type == 'multimodal':
            return MultiModalModel(model_config)
        else:
            raise ValueError(f"Unsupported model type: {model_type}")

class HyperparameterOptimizer:
    def __init__(self):
        self.study_storage = "sqlite:///hyperparameter_studies.db"
        
    async def optimize(self, training_config: Dict, 
                      train_data: DataLoader, 
                      val_data: DataLoader) -> Dict[str, Any]:
        """Optimize hyperparameters using Optuna"""
        
        def objective(trial):
            # Define hyperparameter space
            hyperparams = {
                'learning_rate': trial.suggest_float('learning_rate', 1e-5, 1e-1, log=True),
                'batch_size': trial.suggest_categorical('batch_size', [16, 32, 64, 128]),
                'hidden_size': trial.suggest_int('hidden_size', 128, 1024, step=64),
                'num_layers': trial.suggest_int('num_layers', 2, 8),
                'dropout_rate': trial.suggest_float('dropout_rate', 0.0, 0.5),
                'weight_decay': trial.suggest_float('weight_decay', 1e-6, 1e-2, log=True)
            }
            
            # Update config with trial parameters
            trial_config = {**training_config}
            trial_config['model_config'].update(hyperparams)
            
            # Train model with trial parameters
            model = self._create_model(trial_config['model_config'])
            trainer = pl.Trainer(
                max_epochs=trial_config.get('max_epochs', 10),
                gpus=trial_config.get('gpus', 0),
                logger=False,  # Disable logging for trials
                enable_progress_bar=False,
                enable_model_summary=False
            )
            
            # Train
            trainer.fit(model, train_data, val_data)
            
            # Return validation metric to optimize
            return trainer.callback_metrics['val_accuracy'].item()
            
        # Create study
        study = optuna.create_study(
            direction='maximize',
            storage=self.study_storage,
            study_name=f"study_{training_config.get('model_name', 'default')}",
            load_if_exists=True
        )
        
        # Add MLflow callback
        mlflow_callback = MLflowCallback(
            tracking_uri=mlflow.get_tracking_uri(),
            metric_name="val_accuracy"
        )
        
        # Optimize
        study.optimize(
            objective, 
            n_trials=training_config.get('optimization_trials', 50),
            callbacks=[mlflow_callback]
        )
        
        return study.best_params

class DistributedTrainer:
    def __init__(self):
        self.strategy_configs = {
            'ddp': self._setup_ddp_strategy,
            'deepspeed': self._setup_deepspeed_strategy,
            'fsdp': self._setup_fsdp_strategy
        }
        
    async def train(self, model: nn.Module, train_data: DataLoader,
                   val_data: DataLoader, config: Dict) -> nn.Module:
        """Distributed training with multiple strategies"""
        
        strategy = config.get('distributed_strategy', 'ddp')
        num_gpus = config.get('num_gpus', torch.cuda.device_count())
        
        # Setup strategy
        strategy_config = self.strategy_configs[strategy](config)
        
        # Create Lightning trainer
        trainer = pl.Trainer(
            max_epochs=config.get('max_epochs', 100),
            gpus=num_gpus,
            strategy=strategy_config,
            precision=config.get('precision', 32),
            gradient_clip_val=config.get('gradient_clip_val', 1.0),
            accumulate_grad_batches=config.get('accumulate_grad_batches', 1),
            logger=MLFlowLogger(
                experiment_name=config.get('experiment_name'),
                tracking_uri=mlflow.get_tracking_uri()
            ),
            callbacks=self._create_callbacks(config)
        )
        
        # Train model
        trainer.fit(model, train_data, val_data)
        
        return model
        
    def _setup_ddp_strategy(self, config: Dict):
        """Setup Distributed Data Parallel strategy"""
        return pl.strategies.DDPStrategy(
            find_unused_parameters=config.get('find_unused_parameters', False),
            gradient_as_bucket_view=config.get('gradient_as_bucket_view', True)
        )
        
    def _setup_deepspeed_strategy(self, config: Dict):
        """Setup DeepSpeed strategy for large models"""
        deepspeed_config = {
            "zero_optimization": {
                "stage": config.get('zero_stage', 2),
                "offload_optimizer": {
                    "device": "cpu"
                },
                "offload_param": {
                    "device": "cpu"
                }
            },
            "fp16": {
                "enabled": config.get('fp16', True)
            },
            "gradient_accumulation_steps": config.get('accumulate_grad_batches', 1),
            "train_micro_batch_size_per_gpu": config.get('micro_batch_size', 16)
        }
        
        return pl.strategies.DeepSpeedStrategy(
            config=deepspeed_config
        )
        
    def _create_callbacks(self, config: Dict) -> List:
        """Create training callbacks"""
        callbacks = []
        
        # Early stopping
        if config.get('early_stopping', False):
            callbacks.append(
                pl.callbacks.EarlyStopping(
                    monitor='val_loss',
                    patience=config.get('patience', 10),
                    mode='min'
                )
            )
            
        # Model checkpointing
        callbacks.append(
            pl.callbacks.ModelCheckpoint(
                monitor='val_accuracy',
                mode='max',
                save_top_k=config.get('save_top_k', 3),
                filename='{epoch}-{val_accuracy:.2f}'
            )
        )
        
        # Learning rate monitoring
        callbacks.append(pl.callbacks.LearningRateMonitor())
        
        return callbacks

class ModelValidator:
    def __init__(self):
        self.validation_metrics = {
            'classification': self._classification_metrics,
            'regression': self._regression_metrics,
            'generation': self._generation_metrics,
            'multimodal': self._multimodal_metrics
        }
        
    async def validate(self, model: nn.Module, test_data: DataLoader,
                      config: Dict) -> Dict[str, float]:
        """Comprehensive model validation"""
        
        model.eval()
        task_type = config.get('task_type', 'classification')
        
        # Basic performance metrics
        basic_metrics = await self.validation_metrics[task_type](model, test_data)
        
        # Advanced validation tests
        robustness_metrics = await self._test_robustness(model, test_data)
        fairness_metrics = await self._test_fairness(model, test_data, config)
        efficiency_metrics = await self._test_efficiency(model, test_data)
        
        # Combine all metrics
        validation_results = {
            **basic_metrics,
            **robustness_metrics,
            **fairness_metrics,
            **efficiency_metrics
        }
        
        # Calculate overall performance score
        validation_results['performance_score'] = self._calculate_overall_score(
            validation_results
        )
        
        return validation_results
        
    async def _classification_metrics(self, model: nn.Module, 
                                    test_data: DataLoader) -> Dict[str, float]:
        """Classification-specific validation metrics"""
        from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
        
        all_predictions = []
        all_labels = []
        
        with torch.no_grad():
            for batch in test_data:
                inputs, labels = batch
                outputs = model(inputs)
                predictions = torch.argmax(outputs, dim=1)
                
                all_predictions.extend(predictions.cpu().numpy())
                all_labels.extend(labels.cpu().numpy())
                
        return {
            'accuracy': accuracy_score(all_labels, all_predictions),
            'precision': precision_score(all_labels, all_predictions, average='weighted'),
            'recall': recall_score(all_labels, all_predictions, average='weighted'),
            'f1_score': f1_score(all_labels, all_predictions, average='weighted')
        }
        
    async def _test_robustness(self, model: nn.Module, 
                             test_data: DataLoader) -> Dict[str, float]:
        """Test model robustness to adversarial examples and noise"""
        
        # Test with Gaussian noise
        noise_performance = await self._test_with_noise(model, test_data)
        
        # Test with adversarial examples
        adversarial_performance = await self._test_adversarial_robustness(model, test_data)
        
        return {
            'noise_robustness': noise_performance,
            'adversarial_robustness': adversarial_performance,
            'robustness_score': (noise_performance + adversarial_performance) / 2
        }
        
    async def _test_fairness(self, model: nn.Module, test_data: DataLoader,
                           config: Dict) -> Dict[str, float]:
        """Test model fairness across different groups"""
        
        sensitive_attributes = config.get('sensitive_attributes', [])
        if not sensitive_attributes:
            return {'fairness_score': 1.0}  # No sensitive attributes to test
            
        # This is a simplified fairness test
        # In practice, you'd implement more sophisticated fairness metrics
        
        group_performances = {}
        
        for attribute in sensitive_attributes:
            # Test performance for each group
            group_data = self._filter_by_attribute(test_data, attribute)
            group_performance = await self._classification_metrics(model, group_data)
            group_performances[attribute] = group_performance['accuracy']
            
        # Calculate fairness as minimum performance across groups
        fairness_score = min(group_performances.values()) if group_performances else 1.0
        
        return {
            'fairness_score': fairness_score,
            'group_performances': group_performances
        }

Model Registry & Versioning 

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from mlflow.entities.model_registry import ModelVersion
from typing import Dict, List, Optional
import semver
import hashlib
import json

class AIMatrixModelRegistry:
    def __init__(self, mlflow_tracking_uri: str):
        mlflow.set_tracking_uri(mlflow_tracking_uri)
        self.client = MlflowClient()
        self.staging_policies = StagingPolicies()
        self.deployment_manager = DeploymentManager()
        
    async def register_model(self, model_uri: str, model_name: str,
                           model_metadata: Dict[str, Any]) -> ModelVersion:
        """Register model with comprehensive metadata and versioning"""
        
        # Create model version
        model_version = self.client.create_model_version(
            name=model_name,
            source=model_uri,
            description=model_metadata.get('description', '')
        )
        
        # Add comprehensive tags and metadata
        await self._add_model_metadata(model_version, model_metadata)
        
        # Set initial stage
        initial_stage = await self._determine_initial_stage(model_metadata)
        self.client.transition_model_version_stage(
            name=model_name,
            version=model_version.version,
            stage=initial_stage
        )
        
        # Validate model
        validation_results = await self._validate_registered_model(model_version)
        
        # Store validation results
        for metric_name, value in validation_results.items():
            self.client.log_metric(
                model_version.run_id, 
                f"registry_{metric_name}", 
                value
            )
            
        return model_version
        
    async def promote_model(self, model_name: str, version: str,
                          target_stage: str, validation_data: Optional[Dict] = None) -> bool:
        """Promote model through stages with validation"""
        
        current_version = self.client.get_model_version(model_name, version)
        
        # Check promotion policies
        promotion_allowed = await self.staging_policies.check_promotion_policy(
            current_version, target_stage, validation_data
        )
        
        if not promotion_allowed:
            raise ValueError(f"Promotion to {target_stage} not allowed for model {model_name}:{version}")
            
        # Perform stage-specific validations
        stage_validations = await self._perform_stage_validations(
            current_version, target_stage, validation_data
        )
        
        if not all(stage_validations.values()):
            failed_validations = [k for k, v in stage_validations.items() if not v]
            raise ValueError(f"Stage validation failed: {failed_validations}")
            
        # Execute promotion
        self.client.transition_model_version_stage(
            name=model_name,
            version=version,
            stage=target_stage,
            archive_existing_versions=target_stage == "Production"
        )
        
        # Update deployment if promoting to production
        if target_stage == "Production":
            await self.deployment_manager.deploy_model(model_name, version)
            
        return True
        
    async def compare_model_versions(self, model_name: str,
                                   version1: str, version2: str) -> Dict[str, Any]:
        """Compare two model versions across multiple dimensions"""
        
        v1 = self.client.get_model_version(model_name, version1)
        v2 = self.client.get_model_version(model_name, version2)
        
        comparison = {
            'version_info': {
                'v1': {'version': version1, 'created': v1.creation_timestamp},
                'v2': {'version': version2, 'created': v2.creation_timestamp}
            },
            'performance_comparison': await self._compare_performance(v1, v2),
            'metadata_diff': await self._compare_metadata(v1, v2),
            'model_diff': await self._compare_model_artifacts(v1, v2)
        }
        
        # Generate recommendation
        comparison['recommendation'] = await self._generate_version_recommendation(comparison)
        
        return comparison
        
    async def _add_model_metadata(self, model_version: ModelVersion, 
                                metadata: Dict[str, Any]):
        """Add comprehensive metadata to model version"""
        
        # Core model information
        self.client.set_model_version_tag(
            model_version.name, model_version.version,
            "model_type", metadata.get('model_type', 'unknown')
        )
        
        self.client.set_model_version_tag(
            model_version.name, model_version.version,
            "framework", metadata.get('framework', 'unknown')
        )
        
        # Performance metrics
        if 'performance_metrics' in metadata:
            for metric, value in metadata['performance_metrics'].items():
                self.client.set_model_version_tag(
                    model_version.name, model_version.version,
                    f"metric_{metric}", str(value)
                )
                
        # Training information
        if 'training_info' in metadata:
            training_info = metadata['training_info']
            self.client.set_model_version_tag(
                model_version.name, model_version.version,
                "training_data_hash", training_info.get('data_hash', 'unknown')
            )
            
            self.client.set_model_version_tag(
                model_version.name, model_version.version,
                "training_duration", str(training_info.get('duration_minutes', 0))
            )
            
        # Model signature and schema
        if 'model_signature' in metadata:
            signature_json = json.dumps(metadata['model_signature'])
            signature_hash = hashlib.sha256(signature_json.encode()).hexdigest()
            
            self.client.set_model_version_tag(
                model_version.name, model_version.version,
                "signature_hash", signature_hash
            )
            
    async def _perform_stage_validations(self, model_version: ModelVersion,
                                       target_stage: str, 
                                       validation_data: Optional[Dict]) -> Dict[str, bool]:
        """Perform stage-specific validation tests"""
        
        validations = {}
        
        if target_stage == "Staging":
            # Staging validations
            validations['performance_threshold'] = await self._check_performance_threshold(
                model_version, 'staging'
            )
            validations['integration_test'] = await self._run_integration_tests(
                model_version
            )
            
        elif target_stage == "Production":
            # Production validations (more strict)
            validations['performance_threshold'] = await self._check_performance_threshold(
                model_version, 'production'
            )
            validations['load_test'] = await self._run_load_tests(model_version)
            validations['security_scan'] = await self._run_security_scan(model_version)
            validations['a_b_test_ready'] = await self._check_ab_test_readiness(model_version)
            
        return validations

class StagingPolicies:
    def __init__(self):
        self.policies = {
            'staging_requirements': {
                'min_accuracy': 0.85,
                'max_inference_time': 100,  # milliseconds
                'integration_tests_pass': True
            },
            'production_requirements': {
                'min_accuracy': 0.90,
                'max_inference_time': 50,  # milliseconds
                'load_tests_pass': True,
                'security_scan_pass': True,
                'staging_duration_hours': 24  # Must be in staging for 24h
            }
        }
        
    async def check_promotion_policy(self, model_version: ModelVersion,
                                   target_stage: str, 
                                   validation_data: Optional[Dict]) -> bool:
        """Check if model meets promotion policy requirements"""
        
        if target_stage.lower() not in ['staging', 'production']:
            return True  # No restrictions for other stages
            
        requirements = self.policies.get(f'{target_stage.lower()}_requirements', {})
        
        # Check each requirement
        for requirement, threshold in requirements.items():
            if not await self._check_single_requirement(
                model_version, requirement, threshold, validation_data
            ):
                return False
                
        return True
        
    async def _check_single_requirement(self, model_version: ModelVersion,
                                      requirement: str, threshold: Any,
                                      validation_data: Optional[Dict]) -> bool:
        """Check individual policy requirement"""
        
        if requirement == 'min_accuracy':
            model_accuracy = self._get_model_metric(model_version, 'accuracy')
            return model_accuracy >= threshold
            
        elif requirement == 'max_inference_time':
            inference_time = self._get_model_metric(model_version, 'inference_time_ms')
            return inference_time <= threshold
            
        elif requirement == 'staging_duration_hours':
            staging_time = self._get_staging_duration(model_version)
            return staging_time >= threshold
            
        # Add more requirement checks as needed
        return True

Experiment Tracking & Management 

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import wandb
from tensorboard.compat.v1 import summary
from torch.utils.tensorboard import SummaryWriter
import matplotlib.pyplot as plt
import seaborn as sns

class ExperimentTracker:
    def __init__(self, config: Dict[str, str]):
        self.tracking_backends = {}
        
        # Initialize MLflow
        if 'mlflow' in config:
            mlflow.set_tracking_uri(config['mlflow']['tracking_uri'])
            self.tracking_backends['mlflow'] = MLflowTracker()
            
        # Initialize Weights & Biases
        if 'wandb' in config:
            wandb.init(
                project=config['wandb']['project'],
                entity=config['wandb']['entity'],
                config=config.get('experiment_config', {})
            )
            self.tracking_backends['wandb'] = WandBTracker()
            
        # Initialize TensorBoard
        if 'tensorboard' in config:
            self.tb_writer = SummaryWriter(config['tensorboard']['log_dir'])
            self.tracking_backends['tensorboard'] = TensorBoardTracker(self.tb_writer)
            
        self.experiment_manager = ExperimentManager()
        
    async def start_experiment(self, experiment_config: Dict[str, Any]) -> str:
        """Start new experiment with comprehensive tracking"""
        
        experiment_id = self.experiment_manager.create_experiment(experiment_config)
        
        # Log to all backends
        for backend_name, backend in self.tracking_backends.items():
            await backend.start_run(experiment_id, experiment_config)
            
        return experiment_id
        
    async def log_metrics(self, metrics: Dict[str, float], step: int = None):
        """Log metrics to all tracking backends"""
        
        for backend in self.tracking_backends.values():
            await backend.log_metrics(metrics, step)
            
    async def log_hyperparameters(self, hyperparameters: Dict[str, Any]):
        """Log hyperparameters to all tracking backends"""
        
        for backend in self.tracking_backends.values():
            await backend.log_params(hyperparameters)
            
    async def log_artifacts(self, artifacts: Dict[str, Any]):
        """Log artifacts (models, plots, etc.) to tracking backends"""
        
        for backend in self.tracking_backends.values():
            await backend.log_artifacts(artifacts)
            
    async def log_model_architecture(self, model: nn.Module, input_sample: torch.Tensor):
        """Log model architecture and computational graph"""
        
        # Create architecture visualization
        model_viz = self._create_model_visualization(model, input_sample)
        
        # Log to appropriate backends
        if 'tensorboard' in self.tracking_backends:
            self.tb_writer.add_graph(model, input_sample)
            
        if 'wandb' in self.tracking_backends:
            wandb.watch(model, log_freq=100)
            
        await self.log_artifacts({'model_architecture': model_viz})
        
    async def compare_experiments(self, experiment_ids: List[str]) -> Dict[str, Any]:
        """Compare multiple experiments across metrics and configurations"""
        
        comparison_data = {}
        
        for exp_id in experiment_ids:
            exp_data = await self.experiment_manager.get_experiment_data(exp_id)
            comparison_data[exp_id] = exp_data
            
        # Generate comparison visualizations
        comparison_plots = await self._create_comparison_plots(comparison_data)
        
        # Statistical analysis
        statistical_analysis = await self._perform_statistical_analysis(comparison_data)
        
        return {
            'experiments': comparison_data,
            'comparison_plots': comparison_plots,
            'statistical_analysis': statistical_analysis,
            'recommendations': self._generate_experiment_recommendations(comparison_data)
        }
        
    async def _create_comparison_plots(self, experiment_data: Dict) -> Dict[str, Any]:
        """Create comprehensive comparison plots"""
        
        plots = {}
        
        # Metric comparison plots
        metrics_df = self._extract_metrics_dataframe(experiment_data)
        
        # Performance comparison
        plt.figure(figsize=(12, 8))
        for metric in ['accuracy', 'loss', 'f1_score']:
            if metric in metrics_df.columns:
                sns.lineplot(data=metrics_df, x='step', y=metric, hue='experiment_id')
        plt.title('Performance Metrics Comparison')
        plots['performance_comparison'] = self._save_plot_to_bytes()
        
        # Hyperparameter correlation
        hyperparams_df = self._extract_hyperparameters_dataframe(experiment_data)
        if not hyperparams_df.empty:
            plt.figure(figsize=(10, 8))
            correlation_matrix = hyperparams_df.select_dtypes(include=[np.number]).corr()
            sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
            plt.title('Hyperparameter Correlation Matrix')
            plots['hyperparameter_correlation'] = self._save_plot_to_bytes()
            
        return plots

class ContinualLearner:
    def __init__(self):
        self.learning_strategies = {
            'elastic_weight_consolidation': EWCStrategy(),
            'replay_buffer': ReplayBufferStrategy(),
            'progressive_networks': ProgressiveNetworksStrategy(),
            'meta_learning': MetaLearningStrategy()
        }
        
        self.drift_detector = DriftDetector()
        self.performance_monitor = PerformanceMonitor()
        
    async def setup_continual_learning(self, base_model: nn.Module,
                                     learning_strategy: str,
                                     config: Dict[str, Any]) -> 'ContinualLearningSystem':
        """Set up continual learning system"""
        
        if learning_strategy not in self.learning_strategies:
            raise ValueError(f"Unknown learning strategy: {learning_strategy}")
            
        strategy = self.learning_strategies[learning_strategy]
        
        # Initialize continual learning system
        cl_system = ContinualLearningSystem(
            base_model=base_model,
            strategy=strategy,
            config=config
        )
        
        # Set up monitoring
        await cl_system.setup_monitoring(
            drift_detector=self.drift_detector,
            performance_monitor=self.performance_monitor
        )
        
        return cl_system
        
    async def adapt_to_new_data(self, cl_system: 'ContinualLearningSystem',
                              new_data: DataLoader,
                              task_id: str) -> Dict[str, Any]:
        """Adapt model to new data while preserving previous knowledge"""
        
        # Detect distribution drift
        drift_analysis = await self.drift_detector.analyze_drift(
            cl_system.get_historical_data(),
            new_data
        )
        
        # Determine adaptation strategy based on drift
        adaptation_config = await self._determine_adaptation_strategy(
            drift_analysis, cl_system.config
        )
        
        # Perform adaptation
        adaptation_results = await cl_system.adapt(
            new_data, task_id, adaptation_config
        )
        
        # Validate retention of previous knowledge
        retention_results = await self._validate_knowledge_retention(
            cl_system, task_id
        )
        
        return {
            'drift_analysis': drift_analysis,
            'adaptation_results': adaptation_results,
            'knowledge_retention': retention_results,
            'performance_metrics': await self._compute_continual_learning_metrics(cl_system)
        }

class FederatedLearning:
    def __init__(self, federation_config: Dict[str, Any]):
        self.federation_config = federation_config
        self.aggregation_strategy = self._get_aggregation_strategy(
            federation_config.get('aggregation', 'fedavg')
        )
        self.privacy_engine = PrivacyEngine()
        self.participant_manager = ParticipantManager()
        
    async def setup_federation(self, model_architecture: Dict[str, Any],
                             participants: List[Dict[str, Any]]) -> str:
        """Set up federated learning system"""
        
        federation_id = self.participant_manager.create_federation(
            model_architecture, participants
        )
        
        # Initialize global model
        global_model = await self._initialize_global_model(model_architecture)
        
        # Set up participant clients
        for participant in participants:
            await self.participant_manager.setup_participant_client(
                federation_id, participant, global_model
            )
            
        return federation_id
        
    async def run_federated_round(self, federation_id: str,
                                round_config: Dict[str, Any]) -> Dict[str, Any]:
        """Execute one round of federated learning"""
        
        # Select participants for this round
        selected_participants = await self.participant_manager.select_participants(
            federation_id, round_config
        )
        
        # Distribute global model to participants
        distribution_results = await self._distribute_model(
            federation_id, selected_participants
        )
        
        # Collect local updates
        local_updates = await self._collect_local_updates(
            selected_participants, round_config
        )
        
        # Apply privacy-preserving techniques
        if self.federation_config.get('differential_privacy', False):
            local_updates = await self.privacy_engine.apply_differential_privacy(
                local_updates, self.federation_config['privacy_budget']
            )
            
        # Aggregate updates
        aggregated_update = await self.aggregation_strategy.aggregate(
            local_updates, self.federation_config
        )
        
        # Update global model
        global_model_update = await self._update_global_model(
            federation_id, aggregated_update
        )
        
        return {
            'round_metrics': await self._compute_round_metrics(local_updates),
            'aggregation_info': aggregated_update['metadata'],
            'global_model_performance': global_model_update['performance'],
            'privacy_metrics': await self.privacy_engine.compute_privacy_metrics()
        }
        
    async def _collect_local_updates(self, participants: List[Dict],
                                   config: Dict) -> List[Dict]:
        """Collect model updates from participating clients"""
        
        update_tasks = []
        
        for participant in participants:
            task = self._get_participant_update(participant, config)
            update_tasks.append(task)
            
        # Collect all updates
        local_updates = await asyncio.gather(*update_tasks)
        
        # Validate updates
        validated_updates = []
        for update in local_updates:
            if await self._validate_local_update(update):
                validated_updates.append(update)
                
        return validated_updates

class PrivacyEngine:
    def __init__(self):
        self.dp_mechanisms = {
            'gaussian': self._gaussian_mechanism,
            'laplace': self._laplace_mechanism,
            'exponential': self._exponential_mechanism
        }
        
    async def apply_differential_privacy(self, local_updates: List[Dict],
                                       privacy_budget: float) -> List[Dict]:
        """Apply differential privacy to local updates"""
        
        private_updates = []
        
        for update in local_updates:
            # Add calibrated noise to gradients
            noisy_gradients = await self._add_calibrated_noise(
                update['gradients'], privacy_budget
            )
            
            # Create private update
            private_update = {
                **update,
                'gradients': noisy_gradients,
                'privacy_applied': True,
                'privacy_budget_used': privacy_budget / len(local_updates)
            }
            
            private_updates.append(private_update)
            
        return private_updates
        
    async def _add_calibrated_noise(self, gradients: Dict[str, torch.Tensor],
                                  privacy_budget: float) -> Dict[str, torch.Tensor]:
        """Add calibrated noise to gradients for differential privacy"""
        
        noisy_gradients = {}
        
        for param_name, gradient in gradients.items():
            # Calculate sensitivity (simplified)
            sensitivity = torch.norm(gradient).item()
            
            # Calculate noise scale
            noise_scale = sensitivity / privacy_budget
            
            # Add Gaussian noise
            noise = torch.normal(0, noise_scale, size=gradient.shape)
            noisy_gradients[param_name] = gradient + noise
            
        return noisy_gradients

Monitoring & Feedback Systems 

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from prometheus_client import Counter, Histogram, Gauge
import structlog
from typing import Dict, List, Any
from datetime import datetime, timedelta

class MLModelMonitoring:
    def __init__(self):
        # Prometheus metrics
        self.model_predictions = Counter(
            'ml_model_predictions_total',
            'Total model predictions',
            ['model_name', 'model_version', 'prediction_type']
        )
        
        self.prediction_latency = Histogram(
            'ml_prediction_latency_seconds',
            'Model prediction latency',
            ['model_name', 'model_version']
        )
        
        self.model_accuracy = Gauge(
            'ml_model_accuracy',
            'Current model accuracy',
            ['model_name', 'model_version']
        )
        
        self.drift_score = Gauge(
            'ml_model_drift_score',
            'Model drift detection score',
            ['model_name', 'drift_type']
        )
        
        # Monitoring components
        self.drift_detector = DriftDetector()
        self.performance_analyzer = PerformanceAnalyzer()
        self.feedback_collector = FeedbackCollector()
        
        # Structured logging
        self.logger = structlog.get_logger()
        
    async def monitor_model_performance(self, model_name: str, model_version: str,
                                      predictions: List[Dict], 
                                      ground_truth: Optional[List[Any]] = None):
        """Monitor model performance in real-time"""
        
        # Track basic metrics
        self.model_predictions.labels(
            model_name=model_name,
            model_version=model_version,
            prediction_type='inference'
        ).inc(len(predictions))
        
        # Calculate latency metrics
        avg_latency = np.mean([p.get('latency', 0) for p in predictions])
        self.prediction_latency.labels(
            model_name=model_name,
            model_version=model_version
        ).observe(avg_latency)
        
        # Performance analysis
        if ground_truth:
            performance_metrics = await self.performance_analyzer.analyze(
                predictions, ground_truth
            )
            
            # Update accuracy gauge
            self.model_accuracy.labels(
                model_name=model_name,
                model_version=model_version
            ).set(performance_metrics['accuracy'])
            
            # Log performance details
            self.logger.info(
                "model_performance_updated",
                model_name=model_name,
                model_version=model_version,
                **performance_metrics
            )
            
        # Drift detection
        drift_results = await self.drift_detector.detect_drift(
            model_name, predictions
        )
        
        for drift_type, score in drift_results.items():
            self.drift_score.labels(
                model_name=model_name,
                drift_type=drift_type
            ).set(score)
            
        # Alert if significant drift detected
        if any(score > 0.8 for score in drift_results.values()):
            await self._trigger_drift_alert(model_name, drift_results)
            
    async def collect_feedback_loop(self, model_name: str,
                                  feedback_data: Dict[str, Any]):
        """Implement feedback collection and model improvement loop"""
        
        # Store feedback
        await self.feedback_collector.store_feedback(
            model_name, feedback_data
        )
        
        # Analyze feedback patterns
        feedback_analysis = await self._analyze_feedback_patterns(
            model_name, feedback_data
        )
        
        # Trigger retraining if needed
        if feedback_analysis['retrain_recommended']:
            await self._trigger_model_retraining(
                model_name, feedback_analysis
            )
            
        return feedback_analysis
        
    async def generate_model_health_report(self, model_name: str,
                                         time_window_days: int = 7) -> Dict[str, Any]:
        """Generate comprehensive model health report"""
        
        end_time = datetime.utcnow()
        start_time = end_time - timedelta(days=time_window_days)
        
        # Collect metrics data
        performance_data = await self._collect_performance_data(
            model_name, start_time, end_time
        )
        
        drift_data = await self._collect_drift_data(
            model_name, start_time, end_time
        )
        
        feedback_data = await self._collect_feedback_data(
            model_name, start_time, end_time
        )
        
        # Generate health score
        health_score = await self._calculate_model_health_score(
            performance_data, drift_data, feedback_data
        )
        
        report = {
            'model_name': model_name,
            'report_period': {
                'start': start_time.isoformat(),
                'end': end_time.isoformat()
            },
            'health_score': health_score,
            'performance_summary': performance_data,
            'drift_analysis': drift_data,
            'feedback_summary': feedback_data,
            'recommendations': await self._generate_health_recommendations(
                health_score, performance_data, drift_data, feedback_data
            )
        }
        
        return report

class DriftDetector:
    def __init__(self):
        self.drift_detection_methods = {
            'statistical': self._statistical_drift_detection,
            'adversarial': self._adversarial_drift_detection,
            'reconstruction': self._reconstruction_drift_detection
        }
        
        self.reference_distributions = {}
        
    async def detect_drift(self, model_name: str, 
                         current_data: List[Dict]) -> Dict[str, float]:
        """Detect various types of model drift"""
        
        drift_scores = {}
        
        # Data drift detection
        data_drift = await self._detect_data_drift(model_name, current_data)
        drift_scores['data_drift'] = data_drift
        
        # Concept drift detection
        concept_drift = await self._detect_concept_drift(model_name, current_data)
        drift_scores['concept_drift'] = concept_drift
        
        # Prediction drift detection
        prediction_drift = await self._detect_prediction_drift(model_name, current_data)
        drift_scores['prediction_drift'] = prediction_drift
        
        return drift_scores
        
    async def _detect_data_drift(self, model_name: str,
                               current_data: List[Dict]) -> float:
        """Detect drift in input data distribution"""
        
        if model_name not in self.reference_distributions:
            # Store current data as reference
            await self._store_reference_distribution(model_name, current_data)
            return 0.0
            
        reference_dist = self.reference_distributions[model_name]
        
        # Extract features for comparison
        current_features = self._extract_features(current_data)
        reference_features = reference_dist['features']
        
        # Use multiple drift detection methods
        drift_scores = []
        
        # Kolmogorov-Smirnov test
        ks_score = await self._ks_drift_test(reference_features, current_features)
        drift_scores.append(ks_score)
        
        # Population Stability Index
        psi_score = await self._psi_drift_test(reference_features, current_features)
        drift_scores.append(psi_score)
        
        # Jensen-Shannon divergence
        js_score = await self._js_divergence_test(reference_features, current_features)
        drift_scores.append(js_score)
        
        # Return maximum drift score
        return max(drift_scores)
        
    async def _detect_concept_drift(self, model_name: str,
                                  current_data: List[Dict]) -> float:
        """Detect concept drift using prediction accuracy changes"""
        
        # Get recent performance history
        recent_performance = await self._get_recent_performance(model_name)
        
        if len(recent_performance) < 2:
            return 0.0
            
        # Calculate performance degradation
        current_accuracy = recent_performance[-1]['accuracy']
        baseline_accuracy = np.mean([p['accuracy'] for p in recent_performance[:-1]])
        
        # Normalize drift score
        performance_drop = max(0, baseline_accuracy - current_accuracy)
        drift_score = min(1.0, performance_drop / baseline_accuracy)
        
        return drift_score

Next Steps

The ML/AI Integration component completes the Data & Knowledge Layer by providing:

  1. Feature Engineering & Management - Centralized feature stores with real-time computation
  2. Model Development & Training - Comprehensive MLOps workflows with distributed training
  3. Model Registry & Versioning - Production-ready model management with automated promotion
  4. Experiment Tracking - Multi-backend experiment management and comparison
  5. Continual & Federated Learning - Advanced learning paradigms for evolving systems
  6. Monitoring & Feedback - Real-time model health monitoring with drift detection

This integration ensures that the AIMatrix platform can continuously learn, adapt, and improve its performance while maintaining the highest standards of reliability, security, and compliance.

Related Documentation

The ML/AI Integration component bridges the gap between raw data and intelligent action, making the AIMatrix platform a truly adaptive and learning system that evolves with your business needs.