MLRun: The Complete MLOps Orchestration Framework for Modern AI Development - Deep Dive Guide

# Create isolated environment for MLRun
conda create -n mlrun-env python=3.9
conda activate mlrun-env

# Core MLRun installation with all features
pip install mlrun[complete]

# For specific cloud providers (choose your stack)
pip install mlrun[s3,azure-blob-storage,google-cloud-storage]

# Development dependencies
pip install jupyter
pip install scikit-learn>=1.3.0
pip install pandas>=1.5.0
pip install numpy>=1.21.0

# Visualization and monitoring
pip install plotly
pip install matplotlib
pip install seaborn

# Optional: GPU support for deep learning
pip install torch torchvision # PyTorch
pip install tensorflow-gpu # TensorFlow

import mlrun
import os
from mlrun import code_to_function, mount_v3io

# Configure MLRun for different environments
def setup_mlrun_environment(env_type="development"):
    """Configure MLRun for different deployment scenarios"""
    
    if env_type == "development":
        # Local development with Docker
        mlrun.set_environment(
            api_base_url="http://localhost:8080",
            artifact_path="./artifacts",
            username="developer"
        )
    
    elif env_type == "staging":
        # Staging cluster configuration
        mlrun.set_environment(
            api_base_url="https://mlrun-api-staging.company.com",
            artifact_path="s3://ml-artifacts-staging/",
            access_key=os.getenv("MLRUN_ACCESS_KEY"),
            username=os.getenv("USERNAME")
        )
    
    elif env_type == "production":
        # Production cluster with full security
        mlrun.set_environment(
            api_base_url="https://mlrun-api.company.com",
            artifact_path="s3://ml-artifacts-prod/",
            access_key=os.getenv("MLRUN_PROD_ACCESS_KEY"),
            username=os.getenv("PROD_USERNAME"),
            # Additional security configurations
            verify_ssl=True,
            timeout=30
        )

    print(f"MLRun configured for {env_type} environment")
    return mlrun.get_or_create_project(f"project-{env_type}")

# Initialize development environment
project = setup_mlrun_environment("development")

# Create MLRun namespace with resource quotas
kubectl create namespace mlrun

# Apply resource quotas for production
cat << EOF | kubectl apply -f -
apiVersion: v1
kind: ResourceQuota
metadata:
  name: mlrun-quota
  namespace: mlrun
spec:
  hard:
    requests.cpu: "50"
    requests.memory: 100Gi
    limits.cpu: "100"
    limits.memory: 200Gi
    persistentvolumeclaims: "20"
EOF

# Create service accounts with proper RBAC
kubectl create serviceaccount mlrun-admin -n mlrun

# Add Helm repository for MLRun Community Edition
helm repo add mlrun-ce https://mlrun.github.io/ce
helm repo update

# Create registry credentials for private images
kubectl create secret docker-registry registry-credentials \
  --namespace mlrun \
  --docker-server=your-registry.company.com \
  --docker-username=$REGISTRY_USERNAME \
  --docker-password=$REGISTRY_PASSWORD \
  --docker-email=$REGISTRY_EMAIL

# Production installation with enterprise configurations
helm install mlrun-ce mlrun-ce/mlrun-ce \
  --namespace mlrun \
  --wait \
  --timeout 30m \
  --set global.registry.url=your-registry.company.com \
  --set global.registry.secretName=registry-credentials \
  --set global.externalHostAddress=$(kubectl get nodes -o wide | awk 'NR==2{print $6}') \
  --set nuclio.dashboard.externalIPAddresses={$(kubectl get nodes -o wide | awk 'NR==2{print $6}')} \
  --set mlrun.api.replicas=3 \
  --set mlrun.ui.replicas=2 \
  --set postgresql.primary.persistence.size=100Gi \
  --set minio.persistence.size=500Gi \
  --set global.storage.storageClass=gp3 \
  --values production-values.yaml

global:
  # Enterprise security settings
  security:
    enabled: true
    tls:
      enabled: true
      secretName: mlrun-tls-secret

  # Resource management
  resources:
    api:
      requests:
        cpu: "2000m"
        memory: "4Gi"
      limits:
        cpu: "4000m"
        memory: "8Gi"
    ui:
      requests:
        cpu: "500m"
        memory: "1Gi"
      limits:
        cpu: "1000m"
        memory: "2Gi"

  # High availability configuration
  mlrun:
    api:
      replicas: 3
      autoscaling:
        enabled: true
        minReplicas: 3
        maxReplicas: 10
        targetCPUUtilizationPercentage: 70
    ui:
      replicas: 2
      autoscaling:
        enabled: true
        minReplicas: 2
        maxReplicas: 5

  # Monitoring and observability
  monitoring:
    prometheus:
      enabled: true
    grafana:
      enabled: true
    persistence:
      enabled: true
      size: 10Gi

  # Backup and disaster recovery
  backup:
    enabled: true
    schedule: "0 2 * * *" # Daily at 2 AM 

retention: "30d"

# advanced_data_processor.py
import pandas as pd
import numpy as np
import mlrun
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.impute import SimpleImputer
import joblib
import json

@mlrun.handler()
def advanced_data_processor(
    context,
    dataset_path: mlrun.DataItem,
    target_column: str,
    test_size: float = 0.2,
    validation_size: float = 0.1,
    outlier_threshold: float = 3.0,
    missing_strategy: str = "median",
    feature_selection: bool = True,
    n_features: int = None,
    random_state: int = 42
):
    """
    Advanced data processing pipeline with comprehensive preprocessing
    """
    # Initialize processing components
    scaler = StandardScaler()
    label_encoder = LabelEncoder()
    imputer = SimpleImputer(strategy=missing_strategy)

    # Load and validate data
    try:
        df = dataset_path.as_df()
        context.log_result("original_shape", df.shape)
        context.log_result("original_columns", list(df.columns))
    except Exception as e:
        context.logger.error(f"Failed to load data: {str(e)}")
        raise ValueError(f"Data loading failed: {str(e)}")

    # Data quality assessment
    quality_metrics = {
        "missing_values": df.isnull().sum().to_dict(),
        "duplicate_rows": df.duplicated().sum(),
        "data_types": df.dtypes.to_dict()
    }
    context.log_result("data_quality", quality_metrics)

    # Handle missing values
    numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist()
    categorical_cols = df.select_dtypes(include=['object']).columns.tolist()
    if numeric_cols:
        df[numeric_cols] = imputer.fit_transform(df[numeric_cols])
        # Outlier detection and removal
if numeric_cols and outlier_threshold > 0:
    z_scores = np.abs(stats.zscore(df[numeric_cols]))
    outliers_mask = (z_scores < outlier_threshold).all(axis=1)
    df = df[outliers_mask]
    context.log_result("outliers_removed", len(df) - outliers_mask.sum())

# Encode categorical variables
encoded_cols = []
for col in categorical_cols:
    if col != target_column:
        df[f"{col}_encoded"] = label_encoder.fit_transform(df[col].astype(str))
        encoded_cols.append(f"{col}_encoded")
        # Save encoder for later use
        context.log_model(
            f"{col}_encoder",
            body=label_encoder,
            model_file=f"{col}_encoder.pkl"
        )

# Feature engineering
if len(numeric_cols) > 1:
    # Create interaction features
    for i, col1 in enumerate(numeric_cols[:5]):  # Limit to prevent explosion
        for col2 in numeric_cols[i+1:6]:
            df[f"{col1}_{col2}_interaction"] = df[col1] * df[col2]

# Prepare feature matrix
feature_cols = numeric_cols + encoded_cols
if feature_selection and n_features and len(feature_cols) > n_features:
    from sklearn.feature_selection import SelectKBest, f_classif
    selector = SelectKBest(score_func=f_classif, k=n_features)
    X_selected = selector.fit_transform(df[feature_cols], df[target_column])
    selected_features = [feature_cols[i] for i in selector.get_support(indices=True)]
    feature_cols = selected_features

    # Log feature selection results
    context.log_model("feature_selector", body=selector, model_file="feature_selector")
    context.log_result("selected_features", selected_features)

# Scale features
X = df[feature_cols]
X_scaled = scaler.fit_transform(X)
df_processed = pd.DataFrame(X_scaled, columns=feature_cols, index=df.index)
df_processed[target_column] = df[target_column].values

# Train-validation-test split
X_temp, X_test, y_temp, y_test = train_test_split(
    df_processed[feature_cols],
    df_processed[target_column],
    test_size=test_size,
    random_state=random_state,
    stratify=df_processed[target_column] if df_processed[target_column].dtype == 'obj'
)
val_size_adjusted = validation_size / (1 - test_size)
X_train, X_val, y_train, y_val = train_test_split(
    X_temp, y_temp,
    test_size=val_size_adjusted,
    random_state=random_state,
    stratify=y_temp if y_temp.dtype == 'object' else None
)

# Create final datasets
train_df = pd.concat([X_train, y_train], axis=1)
val_df = pd.concat([X_val, y_val], axis=1)
test_df = pd.concat([X_test, y_test], axis=1)

# Log processing artifacts
context.log_model("scaler", body=scaler, model_file="scaler.pkl")
context.log_dataset("training_data", df=train_df, format="parquet")
context.log_dataset("validation_data", df=val_df, format="parquet")
context.log_dataset("test_data", df=test_df, format="parquet")

# Log comprehensive metrics
processing_metrics = {
    "processed_shape": df_processed.shape,
    "feature_count": len(feature_cols),
    "train_size": len(train_df),
    "validation_size": len(val_df),
    "test_size": len(test_df),
    "feature_columns": feature_cols,
    "processing_strategy": missing_strategy,
    "outlier_threshold": outlier_threshold
}
context.log_results(processing_metrics)

return {
    "train_data": train_df,
    "validation_data": val_df,
    "test_data": test_df,
    "feature_columns": feature_cols,
    "processing_artifacts": {
        "scaler": "scaler.pkl",
        "encoders": [f"{col}_encoder.pkl" for col in categorical_cols if col != targe]
    }
}

# Register the advanced data processor with enterprise configurations
data_processor = project.set_function(
    name="advanced-data-processor",
    func="advanced_data_processor.py",
    kind="job",
    image="mlrun/mlrun",
    handler="advanced_data_processor"
)

# Configure compute resources based on workload
data_processor.with_requests(cpu="2000m", memory="8Gi")
data_processor.with_limits(cpu="4000m", memory="16Gi")

# Add GPU support if needed
# data_processor.gpus(1, gpu_type="nvidia.com/gpu")

# Configure storage volumes
data_processor.apply(mlrun.mount_v3io()) # For Iguazio users
# Or for standard PVC
# data_processor.apply(mlrun.platforms.mount_pvc("data-pvc", "/data"))

# Set environment variables
data_processor.set_env("OMP_NUM_THREADS", "4")
data_processor.set_env("CUDA_VISIBLE_DEVICES", "0")

# Configure security context for enterprise deployments
from kubernetes import client as k8s_client
security_context = k8s_client.V1SecurityContext(
    run_as_user=1000,
    run_as_group=1000,
    run_as_non_root=True
)
data_processor.with_security_context(security_context)

# Add custom requirements
data_processor.build_config(
    requirements=["scikit-learn==1.3.0", "scipy>=1.9.0", "pandas>=1.5.0"],
    base_image="mlrun/mlrun:1.6.0",
    commands=[
        "apt-get update && apt-get install -y build-essential",
        "pip install --upgrade pip"
    ]
)

# Save function configuration
project.save()

# Execute the advanced data processor
processor_run = project.run_function(
    "advanced-data-processor",
    inputs={"dataset_path": "s3://ml-data/raw/customer_data.csv"},
    params={{
        "target_column": "churn",
        "test_size": 0.2,
        "validation_size": 0.1,
        "outlier_threshold": 2.5,
        "missing_strategy": "median",
        "feature_selection": True,
        "n_features": 50,
        "random_state": 42
    }},
    # Resource overrides for this specific run
    resources={"requests": {"cpu": "3000m", "memory": "12Gi"}},
    # Run on specific node types
    node_selector={"node-type": "compute-optimized"},
    # Add tolerations for dedicated nodes
    tolerations=[{
        "key": "dedicated",
        "operator": "Equal",
        "value": "ml-workloads",
        "effect": "NoSchedule"
    }],
    # Set run priority
    priority_class_name="high-priority",
    # Enable spot instances for cost optimization
    preemption_policy="PreemptLowerPriority"
)

# Monitor run progress
print(f"Run status: {processor_run.state()}")
print(f"Run logs: {processor_run.logs()}")

# Access results once complete
if processor_run.state() == "completed":
    training_data = processor_run.outputs["training_data"]
    validation_data = processor_run.outputs["validation_data"]
    test_data = processor_run.outputs["test_data"]
    print(f"Training data artifact: {training_data}")
    print(f"Processing metrics: {processor_run.results}")

import mlrun.feature_store as fs
from mlrun.feature_store.steps import *
import pandas as pd
from datetime import datetime, timedelta

# Define comprehensive user behavior feature set
user_behavior_features = fs.FeatureSet(
    "user-behavior-v2",
    entities=[fs.Entity("user_id")],
    description="Advanced user behavioral features with real-time aggregations",
    timestamp_key="event_timestamp"
)

# Configure advanced aggregation features
aggregation_configs = [
    {
        "name": "transaction_metrics",
        "column": "transaction_amount",
        "operations": ["sum", "count", "mean", "std", "min", "max"],
        "windows": ["1h", "6h", "24h", "7d", "30d"],
        "period": "1h"
    },
    {
        "name": "session_behavior",
        "column": "session_duration_minutes",
        "operations": ["mean", "max", "count"],
        "windows": ["3h", "24h", "7d"],
        "period": "1h"
    },
    {
        "name": "engagement_metrics",
        "column": "page_views",
        "operations": ["sum", "mean"],
        "windows": ["1h", "6h", "24h"],
        "period": "30m"
    }
]

# Add complex aggregations
for config in aggregation_configs:
    user_behavior_features.add_aggregation(
        name=config["name"],
        column=config["column"],
        operations=config["operations"],
        windows=config["windows"],
        period=config["period"]
    )
    # Add calculated features
user_behavior_features.add_feature(
    fs.Feature("avg_transaction_per_session", ValueType.FLOAT),
    fs.Feature("user_segment", ValueType.STRING),
    fs.Feature("risk_score", ValueType.FLOAT),
    fs.Feature("lifetime_value", ValueType.FLOAT)
)

# Define advanced transformation graph
graph = user_behavior_features.graph

# Add custom transformation steps
@graph.to("CalculatedFeatures", name="feature_engineering")
class AdvancedFeatureEngineering:
    def __init__(self):
        self.risk_model = None  # Load pre-trained risk model

    def do(self, event):
        # Calculate derived features
        if event.get("transaction_count_24h", 0) > 0:
            event["avg_transaction_per_session"] = (
                event.get("transaction_sum_24h", 0) /
                event.get("session_count_24h", 1)
            )
        else:
            event["avg_transaction_per_session"] = 0.0

        # User segmentation logic
        ltv = event.get("lifetime_value", 0)
        transaction_frequency = event.get("transaction_count_30d", 0)

        if ltv > 10000 and transaction_frequency > 20:
            event["user_segment"] = "premium"
        elif ltv > 5000 and transaction_frequency > 10:
            event["user_segment"] = "standard"
        else:
            event["user_segment"] = "basic"

        # Risk scoring
        recent_activity = event.get("transaction_count_1h", 0)
        unusual_pattern = recent_activity > event.get("transaction_mean_24h", 0) * 3

        if unusual_pattern:
            event["risk_score"] = min(1.0, event.get("risk_score", 0.1) * 2)
        else:
            event["risk_score"] = max(0.0, event.get("risk_score", 0.1) * 0.9)

        return event

# Configure multiple storage targets for different use cases
user_behavior_features.set_targets([
    # Offline storage for training
    fs.ParquetTarget(
        name="offline",
        path="s3://ml-features/user-behavior/offline/",
        partitioned=True,
        partition_cols=["year", "month", "day"],
        max_events=10000
    ),
    # Online storage for real-time serving
    fs.RedisNoSqlTarget(
        name="online",
        path="redis://feature-store-cluster:6379/0",
        key_bucketing_number=256,  # For sharding
        max_events=1000000
    ),
    # Stream target for real-time monitoring
    fs.StreamTarget(
        name="monitoring",
        path="kafka://feature-monitoring-topic",
        after_state="online"
    )
])

# Configure real-time data source
user_behavior_features.set_source(
    path="kafka://user-events-topic",
    timestamp_key="event_timestamp",
    key_field="user_id"
)

# Save feature set with metadata
user_behavior_features.metadata.labels = {
    "team": "data-science",
    "environment": "production",
    "criticality": "high"
}
user_behavior_features.save()

# Product-level features with advanced analytics
product_features = fs.FeatureSet(
"product-intelligence-v1",
entities=[fs.Entity("product_id"), fs.Entity("category_id")],
description="Product performance and trend analysis features"
)

# Multi-entity aggregations
product_features.add_aggregation(
    name="sales_performance",
    column="sales_amount",
    operations=["sum", "count", "mean"],
    windows=["1d", "7d", "30d"],
    by=["product_id", "category_id"] # Group by multiple entities
)

# Trend analysis features
product_features.add_aggregation(
    name="inventory_turnover",
    column="inventory_level",
    operations=["last", "min", "max"],
    windows=["1d", "7d"],
    period="4h"
)

# Define complex transformation for trend detection
@product_features.graph.to("TrendAnalysis", name="trend_detector")
class ProductTrendAnalyzer:
    def __init__(self):
        self.trend_window = 7 # Days

    def do(self, event):
        # Calculate sales trend
        recent_sales = event.get("sales_sum_7d", 0)
        previous_sales = event.get("sales_sum_14d", 0) - recent_sales

        if previous_sales > 0:
            trend = (recent_sales - previous_sales) / previous_sales
            event["sales_trend_7d"] = trend

            if trend > 0.2:
                event["trend_category"] = "growing"
            elif trend < -0.2:
                event["trend_category"] = "declining"
            else:
                event["trend_category"] = "stable"
        else:
            event["sales_trend_7d"] = 0.0
            event["trend_category"] = "new"

        return event

# Create sophisticated feature vector for recommendation system
recommendation_features = fs.FeatureVector(
    "recommendation-features-v3",
    features=[
        # User behavioral features
        "user-behavior-v2.transaction_sum_24h",
        "user-behavior-v2.transaction_count_7d",
        "user-behavior-v2.session_duration_mean_24h",
        "user-behavior-v2.user_segment",
        "user-behavior-v2.risk_score",
        # Product intelligence features
        "product-intelligence-v1.sales_sum_7d",
        "product-intelligence-v1.inventory_turnover_last_1d",
        "product-intelligence-v1.sales_trend_7d",
        "product-intelligence-v1.trend_category",
        # Cross-feature interactions
        "user-behavior-v2.lifetime_value",
        "product-intelligence-v1.sales_count_30d"
    ],
    description="Comprehensive feature vector for recommendation engine",
    label_feature="conversion_probability"
)

# Configure feature vector with advanced options
recommendation_features.metadata.labels = {
    "model_type": "recommendation",
    "version": "3.0",
    "data_scientist": "team-lead"
}

# Save feature vector
recommendation_features.save()

# Deploy high-performance real-time ingestion service
from mlrun import serving
from mlrun.serving.server import create_graph

# Create advanced ingestion pipeline
ingestion_function = project.set_function(
    name="user-behavior-ingestion",
    kind="serving",
    image="mlrun/mlrun",
    requirements=[
        "kafka-python>=2.0.2",
        "redis>=4.0.0",
        "pandas>=1.5.0"
    ]
)

# Configure for high-throughput ingestion
ingestion_function.with_limits(cpu="4000m", memory="8Gi")
ingestion_function.with_requests(cpu="2000m", memory="4Gi")

# Set up for horizontal scaling
ingestion_function.spec.min_replicas = 3
ingestion_function.spec.max_replicas = 20
ingestion_function.spec.target_cpu = 70

# Deploy the ingestion service with advanced configuration
ingestion_service = user_behavior_features.deploy_ingestion_service(
    source=fs.sources.KafkaSource(
        brokers="kafka-cluster:9092",
        topics=["user-events", "transaction-events"],
        consumer_group="mlrun-ingestion",
        initial_offset="latest",
        max_events=1000,
        key_field="user_id"
    ),
    run_config=fs.RunConfig(
        function=ingestion_function,
        local=False
    ),
    # Advanced ingestion parameters
    max_events=10000,
    flush_after_seconds=30,
    targets=["online", "offline"],
    return_df=False
)

print(f"Ingestion service deployed: {ingestion_service.status}")

# Set up high-performance feature serving
class AdvancedFeatureService:
    def __init__(self):
        self.feature_service = fs.get_online_feature_service(
            recommendation_features,
            # Advanced caching configuration
            cache_ttl=300,  # 5 minutes
            cache_size=10000,
            prefetch_features=True
        )

    async def get_features_batch(self, entity_rows, timeout=1.0):
        """Get features for multiple entities with timeout"""
        try:
            features = await asyncio.wait_for(
                self.feature_service.get_async(entity_rows),
                timeout=timeout
            )
            return features
        except asyncio.TimeoutError:
            # Fallback to cached or default values
            return self._get_fallback_features(entity_rows)

    def get_features_with_fallback(self, entity_rows):
        """Get features with intelligent fallback strategy"""
        try:
            # Try online store first
            features = self.feature_service.get(entity_rows)
            # Validate feature completeness
            if self._validate_features(features):
                return features
            else:
                # Fallback to offline store for missing features
                return self._enrich_from_offline(features, entity_rows)
        except Exception as e:
            logging.warning(f"Feature serving error: {e}")
            return self._get_fallback_features(entity_rows)

    def _validate_features(self, features):
        """Validate that all required features are present"""
        required_features = ["user_segment", "risk_score", "sales_trend_7d"]
        return all(feat in features for feat in required_features)


# Initialize advanced feature serving
feature_service = AdvancedFeatureService()

# Example usage in production
entity_batch = [
    {"user_id": "user_123", "product_id": "prod_456"},
    {"user_id": "user_789", "product_id": "prod_101"}
]

features = feature_service.get_features_with_fallback(entity_batch)

# Import enterprise-grade training function
distributed_trainer = mlrun.import_function("hub://xgb_trainer_v2")

# Configure for distributed training
distributed_trainer.with_limits(cpu="8000m", memory="32Gi")
distributed_trainer.with_replicas(min=2, max=10)

# Set up advanced hyperparameter search space
hyperparameter_grid = {
    # XGBoost parameters
    "n_estimators": [100, 200, 500, 1000],
    "max_depth": [3, 6, 10, 15],
    "learning_rate": [0.01, 0.1, 0.2, 0.3],
    "subsample": [0.8, 0.9, 1.0],
    "colsample_bytree": [0.8, 0.9, 1.0],
    "reg_alpha": [0, 0.1, 1.0],
    "reg_lambda": [1, 1.5, 2.0],

    # Training parameters
    "early_stopping_rounds": [10, 20, 50],
    "eval_metric": ["auc", "logloss"],

    # Cross-validation parameters
    "cv_folds": [3, 5],
    "stratified": [True],
}

# Advanced training run with enterprise configurations
training_run = project.run_function(
    distributed_trainer,
    name="enterprise-model-training",
    inputs={
        "dataset": processor_run.outputs["training_data"],
        "validation_set": processor_run.outputs["validation_data"],
        "test_set": processor_run.outputs["test_data"]
    },
    hyperparams=hyperparameter_grid,
    params={
        "label_columns": "churn",
        "model_name": "churn_predictor_v2",
        "sample_set_size": 10000, # For model monitoring

        # Advanced training configurations
        "optimize_metric": "auc",
        "optimization_direction": "maximize",
        "max_trials": 100,
        "parallel_trials": 4,

        # Model explainability
        "feature_importance": True,
        "shap_analysis": True,
        "partial_dependence": True,

        # Enterprise compliance
        "model_governance": True,
        "audit_trail": True,
        "data_lineage": True
    },
    # Resource scheduling
    selector="max.auc",
    resources={
        "requests": {"cpu": "4000m", "memory": "16Gi"},
        "limits": {"cpu": "8000m", "memory": "32Gi"}
    },
    # Advanced scheduling
    node_selector={"instance-type": "c5.4xlarge"},
    tolerations=[{
        "key": "training-workloads",
        "operator": "Equal",
        "value": "true",
        "effect": "NoSchedule"
    }],
    # Cost optimization
    spot_instances=True,
    preemption_policy="PreemptLowerPriority"
)

# Monitor training progress with advanced metrics
training_metrics = training_run.get_results()
print(f"Best AUC: {training_metrics.get('best_auc', 'N/A')}")
print(f"Best parameters: {training_metrics.get('best_params', 'N/A')}")
print(f"Training duration: {training_metrics.get('training_duration', 'N/A')}")

# Access model artifacts
best_model = training_run.outputs.get("model")
feature_importance = training_run.artifacts.get("feature_importance")
shap_analysis = training_run.artifacts.get("shap_analysis")

# Import model validation function
model_validator = mlrun.import_function("hub://model_validator_enterprise")

# Configure comprehensive validation suite
validation_run = project.run_function(
    model_validator,
    name="enterprise-model-validation",
    inputs={
        "model": training_run.outputs["model"],
        "test_data": processor_run.outputs["test_data"],
        "validation_data": processor_run.outputs["validation_data"],
        "feature_metadata": processor_run.outputs["feature_columns"]
    },
    params={
        "validation_suite": "comprehensive",
        # Performance thresholds
        "min_auc": 0.75,
        "min_precision": 0.70,
        "min_recall": 0.65,
        "max_prediction_latency": 100, # milliseconds

        # Bias and fairness testing
        "fairness_testing": True,
        "protected_attributes": ["age_group", "gender"],
        "fairness_metrics": ["demographic_parity", "equalized_odds"],

        # Robustness testing
        "adversarial_testing": True,
        "data_drift_simulation": True,
        "stress_testing": True,

        # Compliance testing
        "gdpr_compliance": True,
        "model_interpretability": True,
        "right_to_explanation": True
    }
)

# Check validation results
validation_results = validation_run.get_results()
model_approved = validation_results.get("validation_passed", False)

if model_approved:
    print("✅ Model passed all validation tests")
    print(f"Performance score: {validation_results.get('performance_score')}")
    print(f"Fairness score: {validation_results.get('fairness_score')}")
else:
    print("❌ Model failed validation")
    print(f"Failed tests: {validation_results.get('failed_tests', [ ])}")

# Create enterprise-specific function
@mlrun.handler()
def enterprise_model_deployment(
    context,
    model_path: mlrun.DataItem,
    deployment_strategy: str = "blue_green",
    canary_percentage: float = 10.0,
    validation_dataset: mlrun.DataItem = None,
    business_metrics: dict = None,
    compliance_checks: bool = True
):
    """ 
    Enterprise model deployment with advanced strategies
    """
    import json
    import time
    from datetime import datetime

    # Load and validate model
    model = context.get_dataitem(model_path).get_model()

    # Run compliance checks
    if compliance_checks:
        compliance_results = run_compliance_validation(model, context)
        if not compliance_results["passed"]:
            raise ValueError(f"Compliance validation failed: {compliance_results['issues']}")

    # Deploy based on strategy
    if deployment_strategy == "blue_green":
        deployment_result = deploy_blue_green(
            model=model,
            context=context,
            validation_dataset=validation_dataset
        )
    elif deployment_strategy == "canary":
        deployment_result = deploy_canary(
            model=model,
            context=context,
            canary_percentage=canary_percentage,
            business_metrics=business_metrics
        )
    else:
        deployment_result = deploy_standard(model=model, context=context)

    # Log deployment metrics
    context.log_results({
        "deployment_strategy": deployment_strategy,
        "deployment_time": datetime.utcnow().isoformat(),
        "model_version": model.metadata.get("version", "unknown"),
        "endpoint_url": deployment_result["endpoint_url"],
        "health_check_passed": deployment_result["health_check"],
        "compliance_approved": compliance_checks
    })
    return deployment_result
    def run_compliance_validation(model, context):
    """Run enterprise compliance validation"""
    # Implement your enterprise-specific compliance checks
    return {"passed": True, "issues": []}

def deploy_blue_green(model, context, validation_dataset):
    """Implement blue-green deployment strategy"""
    # Your blue-green deployment logic
    return {
        "endpoint_url": "https://model-api.company.com/v2/predict",
        "health_check": True,
        "deployment_type": "blue_green"
    }

def deploy_canary(model, context, canary_percentage, business_metrics):
    """Implement canary deployment strategy"""
    # Your canary deployment logic
    return {
        "endpoint_url": "https://model-api.company.com/v2/predict",
        "health_check": True,
        "deployment_type": "canary",
        "canary_percentage": canary_percentage
    }

# Register custom enterprise function
enterprise_deployer = project.set_function(
    name="enterprise-model-deployer",
    func=enterprise_model_deployment,
    kind="job",
    image="your-registry.com/mlrun-enterprise:latest",
    requirements=[
        "kubernetes>=24.0.0",
        "requests>=2.28.0",
        "pydantic>=1.10.0"
    ]
)

# Configure with enterprise security
enterprise_deployer.with_security_context(
    k8s_client.V1SecurityContext(
        run_as_user=1001,
        run_as_group=1001,
        run_as_non_root=True,
        read_only_root_filesystem=True
    )
)

# Add service account with deployment permissions
enterprise_deployer.spec.service_account = "mlrun-deployer"

import mlrun.serving as serving
import numpy as np
import pandas as pd
from typing import Dict, List, Any
import asyncio
import logging

# Create sophisticated serving pipeline
class EnterpriseChurnPredictionPipeline(serving.V2ModelServer):
    """
    Enterprise-grade churn prediction serving pipeline with:
    - Input validation and sanitization
    - Feature enrichment from multiple sources
    - Model ensemble predictions
    - Business rule application
    - Comprehensive logging and monitoring
    """

    def load(self):
        """Initialize the serving pipeline components"""
        # Load primary model
        self.primary_model = self.get_model("churn_predictor_v2.pkl")

        # Load ensemble models
        self.ensemble_models = {
            "xgb": self.get_model("xgb_churn_model.pkl"),
            "lgb": self.get_model("lgb_churn_model.pkl"),
            "rf": self.get_model("rf_churn_model.pkl")
        }

        # Load preprocessing artifacts
        self.scaler = self.get_model("feature_scaler.pkl")
        self.feature_selector = self.get_model("feature_selector.pkl")

        # Load business rules engine
        self.business_rules = self.load_business_rules()

        # Initialize feature service
        self.feature_service = mlrun.feature_store.get_online_feature_service(
            "recommendation-features-v3"
        )

        # Initialize monitoring
        self.metrics_collector = self.init_metrics_collector()
        logging.info("Enterprise churn prediction pipeline loaded successfully")

    def preprocess(self, request: Dict[str, Any]) -> Dict[str, Any]:
        """
        Advanced preprocessing with validation and enrichment
        """
        start_time = time.time()
        try:
            # Input validation
            validated_input = self.validate_input(request)

            # Extract customer ID
            customer_id = validated_input.get("customer_id")
            if not customer_id:
                raise ValueError("customer_id is required")

            # Enrich with real-time features
            enriched_features = self.enrich_with_features(customer_id, validated_input)

            # Apply preprocessing transformations
            processed_features = self.apply_transformations(enriched_features)

            # Log preprocessing metrics
            preprocessing_time = time.time() - start_time
            self.metrics_collector.log_metric("preprocessing_time", preprocessing_time)

            return {
                "customer_id": customer_id,
                "features": processed_features,
                "metadata": {
                    "preprocessing_time": preprocessing_time,
                    "feature_count": len(processed_features),
                    "enrichment_source": "real_time"
                }
            }
            except Exception as e:
    self.metrics_collector.log_error("preprocessing_error", str(e))
    raise

def predict(self, request: Dict[str, Any]) -> Dict[str, Any]:
    """
    Advanced prediction with ensemble and confidence scoring
    """
    start_time = time.time()
    try:
        features = request["features"]
        customer_id = request["customer_id"]

        # Run ensemble predictions
        ensemble_predictions = {}
        for model_name, model in self.ensemble_models.items():
            pred_proba = model.predict_proba(features.reshape(1, -1))[0]
            ensemble_predictions[model_name] = {
                "churn_probability": pred_proba[1],
                "confidence": max(pred_proba)  # Confidence as max probability
            }

        # Calculate weighted ensemble prediction
        ensemble_result = self.calculate_ensemble_prediction(ensemble_predictions)

        # Apply business rules
        final_prediction = self.apply_business_rules(
            customer_id=customer_id,
            base_prediction=ensemble_result,
            features=features
        )

        # Log prediction metrics
        prediction_time = time.time() - start_time
        self.metrics_collector.log_metric("prediction_time", prediction_time)
        self.metrics_collector.log_prediction(customer_id, final_prediction)

        return {
            "customer_id": customer_id,
            "churn_probability": final_prediction["churn_probability"],
            "risk_level": final_prediction["risk_level"],
            "confidence_score": final_prediction["confidence_score"],
            "ensemble_details": ensemble_predictions,
            "business_rules_applied": final_prediction.get("rules_applied", []),
            "metadata": {
                "prediction_time": prediction_time,
                "model_version": "2.1.0",
                "timestamp": time.time()
            }
        }
    except Exception as e:
        self.metrics_collector.log_error("prediction_error", str(e))
        raise

def postprocess(self, request: Dict[str, Any]) -> Dict[str, Any]:
    """
    Advanced postprocessing with business logic and recommendations
    """
    try:
        # Generate personalized recommendations
        recommendations = self.generate_recommendations(
            customer_id=request["customer_id"],
            churn_probability=request["churn_probability"],
            risk_level=request["risk_level"]
        )
        # Format response according to API contract
response = {
  "prediction": {
    "customer_id": request["customer_id"],
    "churn_probability": round(request["churn_probability"], 4),
    "risk_level": request["risk_level"],
    "confidence": round(request["confidence_score"], 4)
  },
  "recommendations": recommendations,
  "model_info": {
    "version": request["metadata"]["model_version"],
    "prediction_time_ms": round(request["metadata"]["prediction_time"] * 
    "timestamp": request["metadata"]["timestamp"]
  }
}

# Log business metrics
self.metrics_collector.log_business_metric(
  "churn_prediction_served",
  request["risk_level"]
)
return response

except Exception as e:
  self.metrics_collector.log_error("postprocessing_error", str(e))
  raise

def validate_input(self, request: Dict[str, Any]) -> Dict[str, Any]:
  """Validate and sanitize input data"""
  required_fields = ["customer_id"]
  for field in required_fields:
    if field not in request:
      raise ValueError(f"Required field '{field}' is missing")
  # Additional validation logic
  return request

def enrich_with_features(self, customer_id: str, base_input: Dict[str, Any]) -> np.nd:
  """Enrich input with real-time features"""
  try:
    # Get real-time features
    feature_vector = self.feature_service.get([{"customer_id": customer_id}])
    # Combine with input features
    enriched = {**base_input, **feature_vector}
    # Convert to feature array
    feature_array = self.feature_selector.transform([list(enriched.values())])
    return feature_array[0]
  except Exception as e:
    logging.warning(f"Feature enrichment failed for {customer_id}: {e}")
    # Fallback to basic features
    return np.array(list(base_input.values()))

def apply_transformations(self, features: np.ndarray) -> np.ndarray:
  """Apply preprocessing transformations"""
  return self.scaler.transform(features.reshape(1, -1))[0]

def calculate_ensemble_prediction(self, ensemble_predictions: Dict) -> Dict:
  """Calculate weighted ensemble prediction"""
  # Model weights based on validation performance
  model_weights = {"xgb": 0.4, "lgb": 0.35, "rf": 0.25}
  weighted_probability = sum(
    ensemble_predictions[model]["churn_probability"] * model_weights[model]
    for model in model_weights
  )
  avg_confidence = np.mean([
    pred["confidence"] for pred in ensemble_predictions.values()
  ])
  return {
    "churn_probability": weighted_probability,
    "confidence_score": avg_confidence
  }
  def apply_business_rules(self, customer_id: str, base_prediction: Dict, features: np.):
    """Apply business rules to prediction"""
    churn_prob = base_prediction["churn_probability"]
    confidence = base_prediction["confidence_score"]
    rules_applied = []

    # High-value customer rule
    if self.is_high_value_customer(customer_id):
        if churn_prob > 0.3:  # Lower threshold for high-value customers
            churn_prob = min(churn_prob * 1.2, 0.95)  # Increase urgency
            rules_applied.append("high_value_customer_adjustment")

    # Recent interaction rule
    if self.has_recent_positive_interaction(customer_id):
        churn_prob = churn_prob * 0.8  # Reduce churn probability
        rules_applied.append("recent_positive_interaction")

    # Determine risk level
    if churn_prob >= 0.7:
        risk_level = "HIGH"
    elif churn_prob >= 0.4:
        risk_level = "MEDIUM"
    else:
        risk_level = "LOW"

    return {
        "churn_probability": churn_prob,
        "risk_level": risk_level,
        "confidence_score": confidence,
        "rules_applied": rules_applied
    }


def generate_recommendations(self, customer_id: str, churn_probability: float, risk_l):
    """Generate personalized retention recommendations"""
    recommendations = []

    if risk_level == "HIGH":
        recommendations.extend([
            {"action": "immediate_personal_outreach", "priority": 1},
            {"action": "exclusive_offer_25_percent", "priority": 1},
            {"action": "schedule_retention_call", "priority": 2}
        ])
    elif risk_level == "MEDIUM":
        recommendations.extend([
            {"action": "targeted_email_campaign", "priority": 2},
            {"action": "loyalty_program_invite", "priority": 2},
            {"action": "usage_survey", "priority": 3}
        ])
    else:
        recommendations.extend([
            {"action": "general_satisfaction_survey", "priority": 3},
            {"action": "product_recommendation", "priority": 4}
        ])

    return recommendations

# Create and configure enterprise serving function
serving_function = project.set_function(
    name="enterprise-churn-predictor",
    kind="serving",
    image="your-registry.com/mlrun-serving:latest",
    requirements=[
        "scikit-learn==1.3.0",
        "pandas>=1.5.0",
        "numpy>=1.21.0",
        "redis>=4.0.0"
    ]
)

# Configure enterprise-grade resources
serving_function.with_limits(cpu="4000m", memory="8Gi")
serving_function.with_requests(cpu="1000m", memory="2Gi")

# Configure auto-scaling for production load
serving_function.spec.min_replicas = 3
serving_function.spec.max_replicas = 50
serving_function.spec.target_cpu = 70
serving_function.spec.target_memory = 80

# Add models to the serving function
serving_function.add_model(
    "churn_predictor_v2",
    model_path=training_run.outputs["model"],
    class_name="EnterpriseChurnPredictionPipeline"
)

# Configure advanced networking
serving_function.with_annotations({
    "nginx.ingress.kubernetes.io/rate-limit": "1000",
    "nginx.ingress.kubernetes.io/rate-limit-window": "1m"
})

# Set up health checks
serving_function.set_env("HEALTH_CHECK_ENDPOINT", "/health")
serving_function.set_env("READINESS_TIMEOUT", "30")

# Configure monitoring and observability
serving_function.set_env("METRICS_ENABLED", "true")
serving_function.set_env("DETAILED_LOGGING", "true")
serving_function.set_env("TRACE_SAMPLING", "0.1")

# Deploy with blue-green strategy
deployment = project.deploy_function(
    serving_function,
    # Blue-green deployment configuration
    deployment_mode="blue_green",
    readiness_timeout=60,
    # Advanced health checking
    health_check_path="/health",
    health_check_initial_delay=30
)

print(f"Model endpoint deployed: {deployment.status.address}")
print(f"Health check: {deployment.status.state}")

# Set up A/B testing infrastructure
class ABTestingPipeline(serving.V2ModelServer):
    """A/B testing pipeline for model comparison"""
    
    def load(self):
        # Load model variants
        self.model_a = self.get_model("churn_model_v1.pkl")  # Current champion
        self.model_b = self.get_model("churn_model_v2.pkl")  # New challenger

        # A/B testing configuration
        self.ab_config = {
            "traffic_split": 0.9,  # 90% to A, 10% to B
            "minimum_samples": 1000,
            "significance_level": 0.05,
            "metrics": ["accuracy", "precision", "recall", "business_impact"]
        }

        # Initialize experiment tracker
        self.experiment_tracker = ABExperimentTracker()

    def predict(self, request: Dict[str, Any]) -> Dict[str, Any]:
        customer_id = request["customer_id"]

        # Determine which model to use
        model_variant = self.select_model_variant(customer_id)

        if model_variant == "A":
            prediction = self.model_a.predict_proba(request["features"])[0]
            model_version = "1.0"
        else:
            prediction = self.model_b.predict_proba(request["features"])[0]
            model_version = "2.0"

        result = {
            "customer_id": customer_id,
            "churn_probability": prediction[1],
            "model_variant": model_variant,
            "model_version": model_version
        }

        # Log for A/B testing analysis
        self.experiment_tracker.log_prediction(
            customer_id=customer_id,
            model_variant=model_variant,
            prediction=prediction[1],
            features=request["features"]
        )
        return result

    def select_model_variant(self, customer_id: str) -> str:
        """Determine which model variant to use"""
        # Consistent hashing for stable assignment
        import hashlib
        hash_value = int(hashlib.md5(customer_id.encode()).hexdigest(), 16)

        if hash_value % 100 < int(self.ab_config["traffic_split"] * 100):
            return "A"
        else:
            return "B"


# Deploy A/B testing pipeline
ab_serving_function = project.set_function(
    name="churn-ab-testing",
    kind="serving",
    image="mlrun/mlrun"
)

ab_serving_function.add_model(
    "churn_ab_test",
    class_name="ABTestingPipeline"
)

ab_deployment = project.deploy_function(ab_serving_function)

from kfp import dsl
from kfp.components import InputPath, OutputPath
import mlrun
from typing import Dict, List

@dsl.pipeline(
    name="enterprise-ml-pipeline-v3",
    description="Production ML pipeline with advanced orchestration and governance"
)
def enterprise_ml_pipeline(
    # Data parameters
    data_sources: List[str] = ["s3://raw-data/customers/", "s3://raw-data/transactions/"],
    data_validation_rules: str = "strict",
    feature_engineering_version: str = "v2",

    # Training parameters
    model_types: List[str] = ["xgboost", "lightgbm", "random_forest"],
    optimization_budget: int = 100,
    cross_validation_folds: int = 5,

    # Deployment parameters
    deployment_strategy: str = "blue_green",
    canary_percentage: float = 10.0,
    approval_required: bool = True,

    # Governance parameters
    compliance_checks: bool = True,
    bias_testing: bool = True,
    explainability_required: bool = True
):
    # Stage 1: Data Ingestion and Validation
    data_ingestion = mlrun.run_function(
        "enterprise-data-ingestion",
        name="data-ingestion",
        params={
            "data_sources": data_sources,
            "validation_rules": data_validation_rules,
            "quality_thresholds": {
                "completeness": 0.95,
                "consistency": 0.98,
                "validity": 0.99
            }
        },
        outputs=["raw_data", "data_quality_report"]
    )
    # Stage 2: Parallel Feature Engineering
user_features = mlrun.run_function(
    "user-feature-engineering",
    name="user-features",
    inputs={"dataset": data_ingestion.outputs["raw_data"]},
    params={{
        "feature_set": "user_behavioral_v2",
        "time_windows": ["1h", "6h", "24h", "7d", "30d"],
        "aggregation_functions": ["sum", "mean", "std", "count", "max", "min"]
    }},
    outputs=["user_features"]
)

transaction_features = mlrun.run_function(
    "transaction-feature-engineering",
    name="transaction-features",
    inputs={"dataset": data_ingestion.outputs["raw_data"]},
    params={{
        "feature_set": "transaction_patterns_v2",
        "anomaly_detection": True,
        "risk_scoring": True
    }},
    outputs=["transaction_features"]
)

# Stage 3: Feature Integration and Selection
feature_integration = mlrun.run_function(
    "feature-integration",
    name="integrate-features",
    inputs={{
        "user_features": user_features.outputs["user_features"],
        "transaction_features": transaction_features.outputs["transaction_features"]
    }},
    params={{
        "join_strategy": "outer",
        "feature_selection_method": "mutual_info_classif",
        "max_features": 100,
        "correlation_threshold": 0.95
    }},
    outputs=["integrated_features", "feature_metadata"]
)

# Stage 4: Data Quality Validation Gate
with dsl.Condition(
    data_ingestion.outputs["data_quality_report"].get("overall_quality", 0) > 0.90
) as quality_gate:

    # Stage 5: Parallel Model Training
    training_jobs = {}
    for model_type in model_types:
        training_jobs[model_type] = mlrun.run_function(
            f"{model_type}-trainer",
            name=f"train-{model_type}",
            inputs={"dataset": feature_integration.outputs["integrated_features"]},
            params={{
                "model_type": model_type,
                "optimization_trials": optimization_budget // len(model_types),
                "cv_folds": cross_validation_folds,
                "target_metric": "auc",
                "enable_shap": explainability_required
            }},
            outputs=["model", "metrics", "validation_results"]
        )
        # Stage 6: Model Evaluation and Selection
model_evaluation = mlrun.run_function(
    "model-evaluation-ensemble",
    name="evaluate-models",
    inputs={{
        f"{model_type}_model": training_jobs[model_type].outputs["model"]
        for model_type in model_types
    }},
    params={{
        "evaluation_metrics": ["auc", "precision", "recall", "f1", "business_impact"],
        "ensemble_methods": ["voting", "stacking", "blending"],
        "cross_validation": True
    }},
    outputs=["best_model", "ensemble_model", "evaluation_report"]
).after(*training_jobs.values())

# Stage 7: Comprehensive Model Testing
with dsl.Condition(
    model_evaluation.outputs["evaluation_report"].get("best_auc", 0) > 0.75
) as performance_gate:

    # Bias and Fairness Testing
    if bias_testing:
        bias_test = mlrun.run_function(
            "bias-fairness-testing",
            name="bias-testing",
            inputs={{"model": model_evaluation.outputs["best_model"]}},
            params={{
                "protected_attributes": ["age_group", "gender", "geography"],
                "fairness_metrics": ["demographic_parity", "equalized_odds", "calibration"],
                "bias_threshold": 0.1
            }},
            outputs=["bias_report"]
        )

    # Compliance and Governance Testing
    if compliance_checks:
        compliance_test = mlrun.run_function(
            "compliance-testing",
            name="compliance-check",
            inputs={{"model": model_evaluation.outputs["best_model"]}},
            params={{
                "gdpr_compliance": True,
                "right_to_explanation": explainability_required,
                "audit_trail": True,
                "data_lineage": True
            }},
            outputs=["compliance_report"]
        )

    # Stage 8: Model Approval Gate (if required)
    if approval_required:
        approval_task = mlrun.run_function(
            "model-approval-workflow",
            name="model-approval",
            inputs={{
                "model": model_evaluation.outputs["best_model"],
                "evaluation_report": model_evaluation.outputs["evaluation_report"],
                "bias_report": bias_test.outputs["bias_report"] if bias_testing else None,
                "compliance_report": compliance_test.outputs["compliance_report"] if compliance_checks else None
            }},
            params={{
                "approval_workflow": "automated_with_human_oversight",
                "stakeholder_groups": ["data_science", "compliance", "business"],
                "approval_timeout": "48h"
            }},
            outputs=["approval_decision", "approval_metadata"]
        )
        # Stage 9: Conditional Deployment
with dsl.Condition(
    approval_task.outputs["approval_decision"] == "approved"
) as approval_gate:
    deployment_job = mlrun.run_function(
        "enterprise-model-deployer",
        name="model-deployment",
        inputs={"model": model_evaluation.outputs["best_model"]},
        params={{
            "deployment_strategy": deployment_strategy,
            "canary_percentage": canary_percentage,
            "health_check_timeout": 300,
            "rollback_threshold": 0.95  # Rollback if performance drops below 95%
        }},
        outputs=["deployment_endpoint", "deployment_metadata"]
    )
else:
    # Direct deployment without approval
    deployment_job = mlrun.run_function(
        "enterprise-model-deployer",
        name="model-deployment-direct",
        inputs={"model": model_evaluation.outputs["best_model"]},
        params={{
            "deployment_strategy": deployment_strategy,
            "canary_percentage": canary_percentage
        }},
        outputs=["deployment_endpoint", "deployment_metadata"]
    ).after(model_evaluation)

# Stage 10: Post-Deployment Setup
monitoring_setup = mlrun.run_function(
    "setup-model-monitoring",
    name="enable-monitoring",
    inputs={"endpoint": deployment_job.outputs["deployment_endpoint"]},
    params={{
        "drift_detection": True,
        "performance_monitoring": True,
        "business_metrics_tracking": True,
        "alert_thresholds": {{
            "drift_score": 0.2,
            "performance_degradation": 0.1,
            "latency_threshold": 500  # ms
        }}
    }},
    outputs=["monitoring_config"]
).after(deployment_job)

# Stage 11: Integration Testing
integration_test = mlrun.run_function(
    "integration-testing",
    name="post-deployment-testing",
    inputs={"endpoint": deployment_job.outputs["deployment_endpoint"]},
    params={{
        "test_suite": "comprehensive",
        "load_testing": True,
        "concurrent_users": 100,
        "test_duration": "10m"
    }},
    outputs=["test_results"]
).after(deployment_job)
# Stage 12: Pipeline Completion Notification
notification = mlrun.run_function(
    "pipeline-notification",
    name="completion-notification",
    inputs={
        "deployment_metadata": deployment_job.outputs["deployment_metadata"],
        "test_results": integration_test.outputs["test_results"],
        "monitoring_config": monitoring_setup.outputs["monitoring_config"]
    },
    params={
        "notification_channels": ["slack", "email", "teams"],
        "stakeholders": ["ml_team", "business_stakeholders", "ops_team"],
        "include_performance_summary": True
    }
).after(integration_test, monitoring_setup)

# Run the comprehensive pipeline
pipeline_run = project.run(
    workflow_path="enterprise_ml_pipeline",
    arguments={
        "data_sources": ["s3://prod-data/customers/2024/", "s3://prod-data/transactions/2"],
        "model_types": ["xgboost", "lightgbm"],
        "optimization_budget": 200,
        "deployment_strategy": "blue_green",
        "approval_required": True,
        "compliance_checks": True,
        "bias_testing": True
    },
    # Pipeline-level configurations
    resources={ "requests": {"cpu": "2000m", "memory": "4Gi"} },
    ttl_seconds_after_finished=86400, # 24 hours
    backoff_limit=3, # Retry failed steps up to 3 times
    watch=True
)
print(f"Pipeline running: {pipeline_run.state()}")

@dsl.pipeline(
    name="robust-ml-pipeline-with-error-handling",
    description="ML pipeline with comprehensive error handling and recovery"
)
def robust_ml_pipeline(
    fallback_model_path: str = "s3://models/fallback/churn_model_v1.pkl",
    max_retries: int = 3,
    error_notification_webhook: str = "https://hooks.slack.com/..."
):
    # Data processing with retry logic
    data_processing = mlrun.run_function(
        "robust-data-processor",
        name="data-processing",
        params={{
            "max_retries": max_retries,
            "retry_delay": 60,  # seconds
            "fallback_strategy": "use_cached_data"
        }},
        outputs=["processed_data", "processing_status"]
    )

    # Conditional training based on data quality
    with dsl.Condition(
        data_processing.outputs["processing_status"] == "success"
    ) as data_success:
        # Attempt primary training
        primary_training = mlrun.run_function(
            "primary-training",
            name="primary-model-training",
            inputs={{"dataset": data_processing.outputs["processed_data"]}},
            params={{"training_mode": "full"}},
            outputs=["model", "training_status", "metrics"]
        )

        # Check training success
        with dsl.Condition(
            primary_training.outputs["training_status"] == "failed"
        ) as training_failed:
            # Fallback to simpler model
            fallback_training = mlrun.run_function(
                "fallback-training",
                name="fallback-model-training",
                inputs={{"dataset": data_processing.outputs["processed_data"]}},
                params={{"model_type": "linear", "quick_mode": True}},
                outputs=["fallback_model", "fallback_metrics"]
            )

            # Error notification
            error_notification = mlrun.run_function(
                "error-handler",
                name="training-failure-notification",
                params={{
                    "error_type": "training_failure",
                    "webhook_url": error_notification_webhook,
                    "severity": "high"
                }}
            ).after(fallback_training)

    # Alternative data processing path
    with dsl.Condition(
        data_processing.outputs["processing_status"] == "failed"
    ) as data_failed:
        # Use fallback model
        fallback_deployment = mlrun.run_function(
            "deploy-fallback-model",
            name="emergency-fallback-deployment",
            params={{
                "model_path": fallback_model_path,
                "deployment_mode": "emergency"
            }},
            outputs=["emergency_endpoint"]
        )

        # Critical error notification
        critical_notification = mlrun.run_function(
            "critical-error-handler",
            name="data-processing-failure",
            params={{
                "error_type": "data_processing_failure",
                "webhook_url": error_notification_webhook,
                "severity": "critical",
                "escalation_required": True
            }}
        ).after(fallback_deployment)

from mlrun.model_monitoring.applications import ModelMonitoringApplicationBase
from mlrun.common.schemas.model_monitoring import ResultKindApp, ResultStatusApp
import numpy as np
import pandas as pd
from scipy import stats
import json
from typing import Dict, List, Any

class EnterpriseModelMonitoringApp(ModelMonitoringApplicationBase):
    """
    Comprehensive model monitoring application with:
    - Multi-dimensional drift detection
    - Performance degradation analysis
    - Business impact monitoring
    - Automated remediation triggers
    """

    def __init__(self,
                 drift_threshold: float = 0.2,
                 performance_threshold: float = 0.1,
                 business_impact_threshold: float = 0.05,
                 remediation_enabled: bool = True):
        super().__init__()
        self.drift_threshold = drift_threshold
        self.performance_threshold = performance_threshold
        self.business_impact_threshold = business_impact_threshold
        self.remediation_enabled = remediation_enabled

        # Initialize monitoring components
        self.drift_detectors = {
            "ks_test": self.kolmogorov_smirnov_test,
            "psi": self.population_stability_index,
            "wasserstein": self.wasserstein_distance_test,
            "js_divergence": self.jensen_shannon_divergence
        }

    def do_tracking(self, monitoring_context) -> List:
        """Comprehensive model monitoring analysis"""
        results = []
        try:
            # Get current and reference data
            current_data = monitoring_context.get_current_batch()
            reference_data = monitoring_context.get_reference_data()

            if current_data.empty or reference_data.empty:
                return [self._create_warning_result("Insufficient data for monitoring")]

            # 1. Data Drift Detection
            drift_results = self.detect_data_drift(current_data, reference_data, monitori
            results.extend(drift_results)

            # 2. Prediction Drift Analysis
            prediction_drift = self.analyze_prediction_drift(current_data, reference_data
            results.extend(prediction_drift)

            # 3. Performance Analysis
            performance_results = self.analyze_model_performance(current_data, monitoring
            results.extend(performance_results)

            # 4. Business Impact Assessment
            business_impact = self.assess_business_impact(current_data, monitoring_contex
            results.extend(business_impact)

            # 5. Automated Remediation
            if self.remediation_enabled:
                remediation_actions = self.trigger_remediation(results, monitoring_contex
                results.extend(remediation_actions)

        except Exception as e:
            results.append(self._create_error_result(f"Monitoring failed: {str(e)}"))

        return results

    def detect_data_drift(self, current_data, reference_data, monitoring_context) -> List:
        """Advanced multi-method drift detection"""
        drift_results = []
        # Get numerical features
        numerical_features = current_data.select_dtypes(include=[np.number]).columns
        # Feature-level drift detection
        feature_drift_scores = {}
        for feature in numerical_features:
    if feature in reference_data.columns:
        current_values = current_data[feature].dropna()
        reference_values = reference_data[feature].dropna()
        if len(current_values) > 0 and len(reference_values) > 0:
            # Apply multiple drift detection methods
            drift_scores = {}
            for method_name, method_func in self.drift_detectors.items():
                try:
                    score = method_func(reference_values, current_values)
                    drift_scores[method_name] = score
                except Exception as e:
                    monitoring_context.logger.warning(f"Drift detection failed fo")

            # Aggregate drift scores
            if drift_scores:
                avg_drift_score = np.mean(list(drift_scores.values()))
                feature_drift_scores[feature] = {
                    "score": avg_drift_score,
                    "methods": drift_scores,
                    "drift_detected": avg_drift_score > self.drift_threshold
                }

# Overall drift assessment
if feature_drift_scores:
    overall_drift_score = np.mean([f["score"] for f in feature_drift_scores.value])
    drifted_features = [f for f, s in feature_drift_scores.items() if s["drift_de"]]

    # Log detailed drift analysis
    monitoring_context.log_artifact(
        "feature_drift_analysis",
        body=json.dumps(feature_drift_scores, indent=2),
        format="json"
    )

    # Create drift result
    drift_status = ResultStatusApp.anomaly_detected if overall_drift_score > self else None
    drift_results.append({
        "name": "data_drift_detection",
        "value": overall_drift_score,
        "status": drift_status,
        "kind": ResultKindApp.mm_app_anomaly,
        "extra_data": {
            "drifted_features": drifted_features,
            "drift_threshold": self.drift_threshold,
            "total_features_analyzed": len(feature_drift_scores)
        }
    })

    # Feature-specific alerts
    for feature, drift_info in feature_drift_scores.items():
        if drift_info["drift_detected"]:
            drift_results.append({
                "name": f"feature_drift_{feature}",
                "value": drift_info["score"],
                "status": ResultStatusApp.anomaly_detected,
                "kind": ResultKindApp.mm_app_anomaly,
                "extra_data": {"methods_used": list(drift_info["methods"].keys())}
            })

return drift_results

def analyze_prediction_drift(self, current_data, reference_data, monitoring_context):
    """Analyze drift in model predictions"""
    prediction_results = []
    prediction_column = "prediction"  # Assuming predictions are logged

    if prediction_column in current_data.columns and prediction_column in reference_d:
        current_predictions = current_data[prediction_column].dropna()
        reference_predictions = reference_data[prediction_column].dropna()
  if len(current_predictions) > 0 and len(reference_predictions) > 0:
    # Statistical comparison of predictions
    ks_stat, ks_pval = stats.ks_2samp(reference_predictions, current_predicti)

    # Calculate prediction statistics
    current_mean = current_predictions.mean()
    reference_mean = reference_predictions.mean()
    relative_change = abs(current_mean - reference_mean) / reference_mean if reference_mean else 0

    # Determine if significant shift occurred
    prediction_drift_detected = ks_pval < 0.05 or relative_change > 0.1
    prediction_results.append({
        "name": "prediction_drift",
        "value": relative_change,
        "status": ResultStatusApp.anomaly_detected if prediction_drift_detected else ResultStatusApp.no_anomaly,
        "kind": ResultKindApp.mm_app_anomaly,
        "extra_data": {
            "current_mean": current_mean,
            "reference_mean": reference_mean,
            "ks_statistic": ks_stat,
            "ks_pvalue": ks_pval
        }
    })
return prediction_results


def analyze_model_performance(self, current_data, monitoring_context) -> List:
    """Analyze real-time model performance metrics"""
    performance_results = []

    # Analyze prediction latency
    if "prediction_latency" in current_data.columns:
        latency_data = current_data["prediction_latency"].dropna()
        if len(latency_data) > 0:
            avg_latency = latency_data.mean()
            p95_latency = latency_data.quantile(0.95)
            p99_latency = latency_data.quantile(0.99)

            # Check latency thresholds
            latency_alert = p95_latency > 500  # 500ms threshold
            performance_results.append({
                "name": "prediction_latency",
                "value": avg_latency,
                "status": ResultStatusApp.anomaly_detected if latency_alert else ResultStatusApp.no_anomaly,
                "kind": ResultKindApp.mm_app_performance,
                "extra_data": {
                    "p95_latency": p95_latency,
                    "p99_latency": p99_latency,
                    "samples": len(latency_data)
                }
            })

    # Analyze throughput
    if "timestamp" in current_data.columns:
        time_window = monitoring_context.end_infer_time - monitoring_context.start_in
        throughput = len(current_data) / time_window.total_seconds() * 60  # per minute
        performance_results.append({
            "name": "throughput",
            "value": throughput,
            "status": ResultStatusApp.no_anomaly,
            "kind": ResultKindApp.mm_app_performance,
            "extra_data": {
                "requests_per_minute": throughput,
                "time_window_seconds": time_window.total_seconds()
            }
        })

    return performance_results


def assess_business_impact(self, current_data, monitoring_context) -> List:
    """Assess business impact of model performance"""
    business_results = []

    # Business-specific metrics (customize based on use case)
    if "conversion_rate" in current_data.columns:
        current_conversion = current_data["conversion_rate"].mean()

        # Compare with historical benchmark
        benchmark_conversion = 0.15  # Example benchmark
        impact_ratio = (current_conversion - benchmark_conversion) / benchmark_conversion

        business_results.append({
            "name": "business_conversion_impact",
            "value": impact_ratio,
            "status": ResultStatusApp.anomaly_detected if abs(impact_ratio) > self.bu else ResultStatusApp.no_anomaly,
            "kind": ResultKindApp.mm_app_business,
            "extra_data": {
                "current_conversion_rate": current_conversion,
                "benchmark_conversion_rate": benchmark_conversion
            }
        })
        return business_results

def trigger_remediation(self, monitoring_results, monitoring_context) -> List:
    """Trigger automated remediation actions based on monitoring results"""
    remediation_results = []
    # Check if critical issues detected
    critical_issues = [r for r in monitoring_results if r.get("status") == ResultStat]
    if critical_issues:
        # Trigger retraining if data drift detected
        data_drift_issues = [r for r in critical_issues if "drift" in r.get("name", "")]
        if data_drift_issues:
            # Schedule retraining job
            try:
                self.schedule_retraining(monitoring_context)
                remediation_results.append({
                    "name": "retraining_scheduled",
                    "value": 1,
                    "status": ResultStatusApp.no_anomaly,
                    "kind": ResultKindApp.mm_app_remediation,
                    "extra_data": {"trigger": "data_drift_detected"}
                })
            except Exception as e:
                remediation_results.append({
                    "name": "retraining_failed_to_schedule",
                    "value": 0,
                    "status": ResultStatusApp.anomaly_detected,
                    "kind": ResultKindApp.mm_app_remediation,
                    "extra_data": {"error": str(e)}
                })
    return remediation_results


def schedule_retraining(self, monitoring_context):
    """Schedule automatic model retraining"""
    project = monitoring_context.project
    # Trigger retraining pipeline
    retraining_run = project.run(
        workflow_path="retraining_pipeline",
        arguments={
            "trigger_reason": "data_drift_detected",
            "model_endpoint": monitoring_context.model_endpoint.metadata.name,
            "drift_score": "high"  # Pass drift severity
        },
        schedule="now"
    )
    monitoring_context.logger.info(f"Retraining scheduled: {retraining_run.uid}")


# Drift detection methods
def kolmogorov_smirnov_test(self, reference_data, current_data):
    """Kolmogorov-Smirnov test for drift detection"""
    statistic, pvalue = stats.ks_2samp(reference_data, current_data)
    return statistic


def population_stability_index(self, reference_data, current_data, buckets=10):
    """Population Stability Index calculation"""
    try:
        # Create bins based on reference data
        _, bin_edges = np.histogram(reference_data, bins=buckets)
        # Calculate distributions
        ref_counts, _ = np.histogram(reference_data, bins=bin_edges)
        cur_counts, _ = np.histogram(current_data, bins=bin_edges)
        # Convert to percentages
        ref_percents = ref_counts / len(reference_data) + 1e-8
        cur_percents = cur_counts / len(current_data) + 1e-8
        # Calculate PSI
        psi = np.sum((cur_percents - ref_percents) * np.log(cur_percents / ref_percents))
        return psi
    except:
        return 0.0


def wasserstein_distance_test(self, reference_data, current_data):
    """Wasserstein distance calculation"""
    return stats.wasserstein_distance(reference_data, current_data)


def jensen_shannon_divergence(self, reference_data, current_data, bins=50):
    """Jensen-Shannon divergence calculation"""
    try:
        combined_data = np.concatenate([reference_data, current_data])
        bin_edges = np.histogram_bin_edges(combined_data, bins=bins)
        ref_hist, _ = np.histogram(reference_data, bins=bin_edges, density=True)
        cur_hist, _ = np.histogram(current_data, bins=bin_edges, density=True)
        ref_prob = ref_hist / np.sum(ref_hist) + 1e-8
        cur_prob = cur_hist / np.sum(cur_hist) + 1e-8
        m = 0.5 * (ref_prob + cur_prob)
        js_div = 0.5 * stats.entropy(ref_prob, m) + 0.5 * stats.entropy(cur_prob, m)
        return js_div
    except:
        return 0.0


def _create_warning_result(self, message):
    """Create warning result"""
    return {
        "name": "monitoring_warning",
        "value": 0,
        "status": ResultStatusApp.irrelevant,
        "kind": ResultKindApp.mm_app_anomaly,
        "extra_data": {"message": message}
    }
    def _create_error_result(self, message):
    """Create error result"""
    return {
        "name": "monitoring_error",
        "value": 1,
        "status": ResultStatusApp.anomaly_detected,
        "kind": ResultKindApp.mm_app_anomaly,
        "extra_data": {"error": message}
    }

# Deploy the enterprise monitoring application
enterprise_monitoring_app = project.set_model_monitoring_function(
    func=EnterpriseModelMonitoringApp(
        drift_threshold=0.15,
        performance_threshold=0.1,
        business_impact_threshold=0.05,
        remediation_enabled=True
    ),
    name="enterprise-model-monitoring",
    image="mlrun/mlrun",
    requirements=["scipy>=1.9.0", "pandas>=1.5.0"]
)

# Enable comprehensive model monitoring
project.enable_model_monitoring(
    base_period=5,  # Check every 5 minutes
    default_batch_image="mlrun/mlrun:1.6.0",
    deploy_histogram_data_drift_app=True,
    deploy_drift_stream=True
)

# Configure monitoring for specific endpoint
serving_fn.set_tracking(
    stream_path="kafka://model-monitoring-stream",
    batch_window="1h",
    sample_window="10m"
)

print("Advanced model monitoring enabled")

from mlrun.alerts import AlertConfig, AlertSeverity
from mlrun.common.schemas.alert import EventKind, EventEntityKind

# Configure comprehensive alerting system
def setup_enterprise_alerts(project, model_endpoint_id):
    """
    Set up comprehensive alerting for enterprise model monitoring
    """

    # High-severity alerts
    high_severity_alerts = [
        {
            "name": "critical_data_drift",
            "summary": "Critical data drift detected - immediate attention required",
            "severity": AlertSeverity.HIGH,
            "events": [EventKind.DATA_DRIFT_DETECTED],
            "condition": "drift_score > 0.3",
            "notifications": [
                "slack://ml-alerts-critical",
                "email://ml-team-leads@company.com",
                "pagerduty://ml-oncall"
            ],
            "auto_remediation": True
        },
        {
            "name": "model_performance_degradation",
            "summary": "Significant model performance degradation detected",
            "severity": AlertSeverity.HIGH,
            "events": [EventKind.MODEL_PERFORMANCE_DRIFT_DETECTED],
            "condition": "performance_drop > 0.15",
            "notifications": [
                "slack://ml-alerts-critical",
                "email://business-stakeholders@company.com"
            ],
            "escalation_policy": "ml_team_escalation"
        }
    ]

    # Medium-severity alerts
    medium_severity_alerts = [
        {
            "name": "feature_drift_warning",
            "summary": "Feature drift detected in multiple features",
            "severity": AlertSeverity.MEDIUM,
            "events": [EventKind.DATA_DRIFT_DETECTED],
            "condition": "drifted_features_count > 3",
            "notifications": [
                "slack://ml-alerts",
                "email://data-science-team@company.com"
            ]
        },
        {
            "name": "prediction_latency_high",
            "summary": "Model prediction latency exceeding SLA",
            "severity": AlertSeverity.MEDIUM,
            "events": [EventKind.MODEL_PERFORMANCE_DRIFT_DETECTED],
            "condition": "p95_latency > 500",
            "notifications": ["slack://ml-alerts-performance"]
        }
    ]
    
# Low-severity alerts
low_severity_alerts = [
{
  "name": "throughput_anomaly",
  "summary": "Unusual traffic pattern detected",
  "severity": AlertSeverity.LOW,
  "events": [EventKind.DATA_DRIFT_DETECTED],
  "condition": "throughput_change > 0.5",
  "notifications": ["slack://ml-monitoring"]
}
]

# Create and store all alerts
all_alerts = high_severity_alerts + medium_severity_alerts + low_severity_alerts

for alert_config in all_alerts:
    alert = AlertConfig(
        project=project.metadata.name,
        name=alert_config["name"],
        summary=alert_config["summary"],
        severity=alert_config["severity"],
        entities={
            "kind": EventEntityKind.MODEL_ENDPOINT_RESULT,
            "project": project.metadata.name,
            "ids": [model_endpoint_id]
        },
        trigger={
            "events": alert_config["events"]
        },
        notifications=alert_config["notifications"]
    )
    project.store_alert_config(alert)
    print(f"✅ Alert configured: {alert_config['name']}")

# Set up alerts for deployed model
setup_enterprise_alerts(project, deployment.status.model_endpoint_id)

# mlrun-rbac.yaml - Enterprise RBAC Configuration
apiVersion: v1
kind: ServiceAccount
metadata:
  name: mlrun-data-scientist
  namespace: mlrun
---
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
  namespace: mlrun
  name: mlrun-data-scientist
rules:
- apiGroups: [""]
  resources: ["pods", "pods/log", "configmaps", "secrets"]
  verbs: ["get", "list", "create", "update", "patch", "delete"]
- apiGroups: ["apps"]
  resources: ["deployments"]
  verbs: ["get", "list", "create", "update", "patch"]
- apiGroups: ["mlrun.org"]
  resources: ["functions", "models", "features"]
  verbs: ["get", "list", "create", "update", "patch"]
---
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: mlrun-data-scientist-binding
  namespace: mlrun
subjects:
- kind: ServiceAccount
  name: mlrun-data-scientist
  namespace: mlrun
roleRef:
  kind: Role
  name: mlrun-data-scientist
  apiGroup: rbac.authorization.k8s.io

from kubernetes import client as k8s_client

def configure_enterprise_security(function):
    """Configure enterprise-grade security for MLRun functions"""

    # Security context with non-root user
    security_context = k8s_client.V1SecurityContext(
        run_as_user=1000,
        run_as_group=1000,
        run_as_non_root=True,
        read_only_root_filesystem=True,
        allow_privilege_escalation=False,
        capabilities=k8s_client.V1Capabilities(
            drop=["ALL"]
        ),
        seccomp_profile=k8s_client.V1SeccompProfile(type="RuntimeDefault")
    )
    function.with_security_context(security_context)

    # Pod security context
    pod_security_context = k8s_client.V1PodSecurityContext(
        run_as_user=1000,
        run_as_group=1000,
        run_as_non_root=True,
        fs_group=2000,
        seccomp_profile=k8s_client.V1SeccompProfile(type="RuntimeDefault")
    )
    function.spec.pod_security_context = pod_security_context

    # Network policies
    function.with_annotations({
        "network-policy.kubernetes.io/ingress": "restricted",
        "network-policy.kubernetes.io/egress": "restricted"
    })

    # Resource constraints for security
    function.with_limits(cpu="4000m", memory="8Gi")
    function.with_requests(cpu="100m", memory="256Mi")

    return function

# Apply security to all functions
secure_function = configure_enterprise_security(data_processor)

# Enterprise secrets management
def setup_secure_secrets(project):
    """Set up secure secrets management"""

    # Database credentials
    project.set_secrets({
        "DB_CONNECTION_STRING": "postgresql://user:pass@db-host:5432/mlrun",
        "REDIS_URL": "redis://redis-cluster:6379/0",
        "S3_ACCESS_KEY": "AKIA...",
        "S3_SECRET_KEY": "...",
        "API_KEY": "secret-api-key"
    }, provider="kubernetes")

    # TLS certificates
    project.create_secret(
        "tls-certificate",
        secret_data={
            "tls.crt": "certificate-content",
            "tls.key": "private-key-content"
        },
        secret_type="kubernetes.io/tls"
    )

    # Docker registry credentials
    project.create_secret(
        "docker-registry-secret",
        secret_data={
            ".dockerconfigjson": json.dumps({
                "auths": {
                    "your-registry.com": {
                        "username": "registry-user",
                        "password": "registry-pass",
                        "auth": "encoded-auth"
                    }
                }
            })
        },
        secret_type="kubernetes.io/dockerconfigjson"
    )

    # Configure secure function with secrets
    secure_function.with_secrets("kubernetes", ["DB_CONNECTION_STRING", "API_KEY"])

# Enterprise auto-scaling configuration
def configure_enterprise_scaling(function, workload_type="ml_training"):
    """Configure enterprise-grade auto-scaling and resource management"""
    scaling_configs = {
        "ml_training": {
            "min_replicas": 1,
            "max_replicas": 20,
            "target_cpu": 70,
            "target_memory": 80,
            "scale_up_delay": "30s",
            "scale_down_delay": "300s",
            "requests": {"cpu": "2000m", "memory": "8Gi"},
            "limits": {"cpu": "8000m", "memory": "32Gi"}
        },
        "model_serving": {
            "min_replicas": 3,
            "max_replicas": 50,
            "target_cpu": 60,
            "target_memory": 70,
            "scale_up_delay": "10s",
            "scale_down_delay": "60s",
            "requests": {"cpu": "500m", "memory": "2Gi"},
            "limits": {"cpu": "2000m", "memory": "4Gi"}
        },
        "feature_serving": {
            "min_replicas": 2,
            "max_replicas": 30,
            "target_cpu": 65,
            "target_memory": 75,
            "scale_up_delay": "15s",
            "scale_down_delay": "120s",
            "requests": {"cpu": "1000m", "memory": "4Gi"},
            "limits": {"cpu": "4000m", "memory": "8Gi"}
        }
    }
    config = scaling_configs.get(workload_type, scaling_configs["ml_training"])

    # Configure auto-scaling
    function.spec.min_replicas = config["min_replicas"]
    function.spec.max_replicas = config["max_replicas"]
    function.spec.target_cpu = config["target_cpu"]

    # Resource allocation
    function.with_requests(**config["requests"])
    function.with_limits(**config["limits"])

    # Advanced scaling behavior
    function.with_annotations({
        "autoscaling.knative.dev/scale-up-delay": config["scale_up_delay"],
        "autoscaling.knative.dev/scale-down-delay": config["scale_down_delay"],
        "autoscaling.knative.dev/target-utilization-percentage": str(config["target_cpu"])
    })

    # Node affinity for optimal placement
    function.with_node_selection({
        "node.kubernetes.io/instance-type": "c5.2xlarge",
        "topology.kubernetes.io/zone": "us-west-2a"
    })

    # Pod disruption budget
    function.with_annotations({
        "pod-disruption-budget.kubernetes.io/min-available": "50%"
    })

    return function

# Apply scaling configuration
scaled_serving_function = configure_enterprise_scaling(
    serving_function,
    workload_type="model_serving"
)

# Multi-cloud deployment configuration
class MultiCloudMLRunDeployment:
    """Manage MLRun deployments across multiple cloud providers"""

    def __init__(self):
        self.cloud_configs = {
            "aws": {
                "cluster_endpoint": "https://eks-cluster.us-west-2.amazonaws.com",
                "storage_class": "gp3",
                "node_groups": ["ml-optimized", "cpu-optimized"],
                "availability_zones": ["us-west-2a", "us-west-2b", "us-west-2c"]
            },
            "azure": {
                "cluster_endpoint": "https://aks-cluster.eastus.cloudapp.azure.com",
                "storage_class": "premium-ssd",
                "node_groups": ["ml-nodes", "general-purpose"],
                "availability_zones": ["1", "2", "3"]
            },
            "gcp": {
                "cluster_endpoint": "https://gke-cluster.us-central1.googleapis.com",
                "storage_class": "ssd",
                "node_groups": ["ml-pool", "preemptible-pool"],
                "availability_zones": ["us-central1-a", "us-central1-b", "us-central1-c"]
            }
        }

    def deploy_to_cloud(self, project, cloud_provider, deployment_config):
        """Deploy MLRun project to specific cloud provider"""
        cloud_config = self.cloud_configs[cloud_provider]

        # Set cloud-specific configurations
        project.set_annotation("cloud.provider", cloud_provider)
        project.set_annotation("cluster.endpoint", cloud_config["cluster_endpoint"])

        # Configure storage
        for function in project.functions:
            function.with_annotation("storage.class", cloud_config["storage_class"])

            # Cloud-specific optimizations
            if cloud_provider == "aws":
                function.with_node_selection({"node.kubernetes.io/instance-type": "c5.2xl"})
                function.with_annotation("cluster-autoscaler.kubernetes.io/safe-to-evict")
            elif cloud_provider == "azure":
                function.with_node_selection({"agentpool": "ml-nodes"})
                function.with_annotation("kubernetes.azure.com/scalesetpriority", "regular")
            elif cloud_provider == "gcp":
                function.with_node_selection({"cloud.google.com/machine-family": "c2"})
                function.with_annotation("cluster-autoscaler.kubernetes.io/safe-to-evict")

        # Deploy with cloud-specific parameters
        deployment_result = project.deploy(
            target=cloud_provider,
            wait_for_completion=True,
            **deployment_config
        )
        return deployment_result

    def setup_cross_cloud_replication(self, primary_cloud, backup_clouds):
        """Set up cross-cloud replication for high availability"""

        # Configure data replication
        replication_config = {
            "primary": primary_cloud,
            "replicas": backup_clouds,
            "sync_interval": "1h",
            "consistency_level": "eventual"
        }
        # Set up model registry replication
model_registry_replication = {
  "enabled": True,
  "cross_region": True,
  "encryption": True
}

return {
  "data_replication": replication_config,
  "model_registry": model_registry_replication
}

# Deploy to multiple clouds
multi_cloud_deployer = MultiCloudMLRunDeployment()

# Primary deployment on AWS
aws_deployment = multi_cloud_deployer.deploy_to_cloud(
  project,
  "aws",
  {
    "high_availability": True,
    "auto_scaling": True,
    "monitoring": "enhanced"
  }
)

# Backup deployment on Azure
azure_deployment = multi_cloud_deployer.deploy_to_cloud(
  project,
  "azure",
  {
    "high_availability": False,
    "auto_scaling": True,
    "monitoring": "standard"
  }
)

# .github/workflows/enterprise-ml-pipeline.yml
name: Enterprise ML Pipeline

on:
  push:
    branches: [main, develop]
  pull_request:
    branches: [main]
  schedule:
    - cron: '0 2 * * *' # Daily retraining

env:
  MLRUN_DBPATH: ${{ secrets.MLRUN_DBPATH }}
  MLRUN_ACCESS_KEY: ${{ secrets.MLRUN_ACCESS_KEY }}
  DOCKER_REGISTRY: your-registry.company.com

jobs:
  security-scan:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Security scan
        uses: securecodewarrior/github-action-security-scan@v1
        with:
          api-key: ${{ secrets.SCW_API_KEY }}
      - name: Dependency vulnerability scan
        run: |
          pip install safety
          safety check -r requirements.txt

  code-quality:
    runs-on: ubuntu-latest
    needs: security-scan
    steps:
      - uses: actions/checkout@v3
      - name: Code quality analysis
        uses: sonarqube-quality-gate-action@master
        env:
          SONAR_TOKEN: ${{ secrets.SONAR_TOKEN }}

  build-and-test:
    runs-on: ubuntu-latest
    needs: [security-scan, code-quality]
    steps:
      - uses: actions/checkout@v3
      - name: Setup Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.9'
      - name: Install dependencies
        run: |
          pip install mlrun[complete]
          pip install -r requirements.txt
      - name: Run unit tests
        run: |
          pytest tests/unit/ --cov=src --cov-report=xml
      - name: Run integration tests
        run: |
          pytest tests/integration/ --maxfail=1

  ml-pipeline-staging:
    runs-on: ubuntu-latest
    needs: build-and-test
    if: github.ref == 'refs/heads/develop'
    steps:
      - uses: actions/checkout@v3
      - name: Deploy to staging
        run: |
          python -m mlrun project ./ \
          --run-pipeline enterprise_ml_pipeline \
          --env staging \
          --wait \
          --arguments '{
            "deployment_strategy": "blue_green",
            "approval_required": false,
            "compliance_checks": true
          }'
          ml-pipeline-production:
  runs-on: ubuntu-latest
  needs: build-and-test
  if: github.ref == 'refs/heads/main'
  environment: production
  steps:
    - uses: actions/checkout@v3
    - name: Deploy to production
      run: |
        python -m mlrun project ./ \
          --run-pipeline enterprise_ml_pipeline \
          --env production \
          --wait \
          --arguments '{
            "deployment_strategy": "blue_green",
            "approval_required": true,
            "compliance_checks": true,
            "bias_testing": true,
            "explainability_required": true
          }'
    - name: Post-deployment validation
      run: |
        python scripts/validate_deployment.py \
          --endpoint ${{ env.MODEL_ENDPOINT }} \
          --test-data data/validation/test_cases.json
    - name: Update documentation
      run: |
        python scripts/update_model_catalog.py \
          --model-version ${{ github.sha }} \
          --deployment-timestamp $(date -Iseconds)

monitoring-setup:
  runs-on: ubuntu-latest
  needs: ml-pipeline-production
  if: github.ref == 'refs/heads/main'
  steps:
    - name: Enable enhanced monitoring
      run: |
        python scripts/setup_production_monitoring.py \
          --model-endpoint ${{ env.MODEL_ENDPOINT }} \
          --monitoring-level enterprise \
          --alert-channels slack,email,pagerduty

# Financial risk assessment pipeline
class FinancialRiskAssessmentPipeline(serving.V2ModelServer):
    """Real-time financial risk assessment with regulatory compliance"""

    def load(self):
        # Load multiple specialized models
        self.fraud_model = self.get_model("fraud_detection_v3.pkl")
        self.credit_risk_model = self.get_model("credit_risk_v2.pkl")
        self.aml_model = self.get_model("aml_screening_v1.pkl")
        self.market_risk_model = self.get_model("market_risk_v1.pkl")

        # Load regulatory rules engine
        self.regulatory_engine = RegulatoryComplianceEngine()

        # Initialize real-time feature services
        self.customer_features = mlrun.feature_store.get_online_feature_service("customer")
        self.transaction_features = mlrun.feature_store.get_online_feature_service("transaction")
        self.market_features = mlrun.feature_store.get_online_feature_service("market-context")

    def predict(self, request):
        transaction = request["transaction"]
        customer_id = transaction["customer_id"]

        # Get real-time features
        customer_profile = self.customer_features.get([{"customer_id": customer_id}])
        transaction_context = self.transaction_features.get([{"customer_id": customer_id, "merchant_id": transaction["merchant_id"]}])
        market_conditions = self.market_features.get([{"timestamp": transaction["timestamp"]}])

        # Combined feature vector
        features = {**transaction, **customer_profile, **transaction_context, **market_conditions}

        # Multi-model risk assessment
        risk_scores = {
            "fraud_score": self.fraud_model.predict_proba(features)[0][1],
            "credit_risk": self.credit_risk_model.predict_proba(features)[0][1],
            "aml_risk": self.aml_model.predict_proba(features)[0][1],
            "market_risk": self.market_risk_model.predict(features)[0]
        }

        # Apply regulatory compliance rules
        compliance_result = self.regulatory_engine.evaluate(
            transaction=transaction,
            risk_scores=risk_scores,
            customer_profile=customer_profile
        )

        # Calculate overall risk and decision
        overall_risk = self.calculate_composite_risk(risk_scores)
        decision = self.make_risk_decision(overall_risk, compliance_result)

        return {
            "transaction_id": transaction["transaction_id"],
            "overall_risk_score": overall_risk,
            "individual_risks": risk_scores,
            "decision": decision,
            "compliance_status": compliance_result,
            "processing_time": time.time() - request.get("start_time", time.time())
        }

# Clinical decision support system
class ClinicalDecisionSupportPipeline(serving.V2ModelServer):
    """ HIPAA-compliant clinical decision support with explainability """

    def load(self):
        # Load clinical models
        self.diagnosis_model = self.get_model("clinical_diagnosis_v2.pkl")
        self.treatment_recommendation = self.get_model("treatment_optimizer_v1.pkl")
        self.risk_stratification = self.get_model("patient_risk_v2.pkl")

        # Load medical knowledge base
        self.knowledge_base = MedicalKnowledgeBase()
        self.drug_interaction_checker = DrugInteractionChecker()

        # HIPAA compliance components
        self.audit_logger = HIPAAAuditLogger()
        self.data_anonymizer = MedicalDataAnonymizer()

    def predict(self, request):
        # Anonymize patient data for compliance
        anonymized_data = self.data_anonymizer.anonymize(request["patient_data"])

        # Log access for HIPAA compliance
        self.audit_logger.log_access(
            user_id=request["user_id"],
            patient_id=request["patient_data"]["patient_id"],
            access_type="clinical_decision_support"
        )

        # Extract clinical features
        clinical_features = self.extract_clinical_features(anonymized_data)

        # Multi-model predictions
        diagnosis_prediction = self.diagnosis_model.predict_proba(clinical_features)
        risk_score = self.risk_stratification.predict(clinical_features)[0]

        # Generate treatment recommendations
        treatment_options = self.treatment_recommendation.predict(clinical_features)

        # Check drug interactions
        interaction_warnings = self.drug_interaction_checker.check_interactions(
            current_medications=anonymized_data.get("medications", []),
            proposed_treatments=treatment_options
        )

        # Generate explanations for clinical decisions
        explanations = self.generate_clinical_explanations(
            diagnosis_prediction,
            clinical_features,
            treatment_options
        )

        return {
            "diagnosis_probabilities": diagnosis_prediction,
            "risk_stratification": risk_score,
            "treatment_recommendations": treatment_options,
            "drug_interaction_warnings": interaction_warnings,
            "clinical_explanations": explanations,
            "confidence_intervals": self.calculate_confidence_intervals(),
            "evidence_quality": self.assess_evidence_quality(clinical_features)
        }

class EnterpriseDeploymentValidator:
    """Comprehensive validation for enterprise MLRun deployments"""

    def __init__(self):
        self.validation_checks = [
            self.validate_security_configuration,
            self.validate_resource_allocation,
            self.validate_monitoring_setup,
            self.validate_backup_strategy,
            self.validate_compliance_requirements,
            self.validate_disaster_recovery,
            self.validate_performance_requirements,
            self.validate_integration_points
        ]

    def validate_deployment(self, project, deployment_config):
        """Run comprehensive deployment validation"""
        validation_results = {
            "overall_status": "PASSED",
            "checks": {},
            "recommendations": [],
            "critical_issues": []
        }
        for check in self.validation_checks:
            try:
                result = check(project, deployment_config)
                validation_results["checks"][check.__name__] = result
                if result["status"] == "FAILED":
                    validation_results["overall_status"] = "FAILED"
                    validation_results["critical_issues"].extend(result.get("issues", []))
                    validation_results["recommendations"].extend(result.get("recommendations", []))
            except Exception as e:
                validation_results["checks"][check.__name__] = {
                    "status": "ERROR",
                    "error": str(e)
                }
                validation_results["overall_status"] = "FAILED"
        return validation_results

    def validate_security_configuration(self, project, config):
        """Validate security configuration"""
        issues = []
        recommendations = []

        # Check RBAC configuration
        if not config.get("rbac_enabled"):
            issues.append("RBAC not enabled")

        # Check security contexts
        for function in project.functions:
            if not hasattr(function.spec, 'security_context'):
                issues.append(f"Security context not configured for {function.metadata.name}")

        # Check secrets management
        if not project.secrets:
            recommendations.append("Consider using Kubernetes secrets for sensitive data")

        # Check network policies
        if not config.get("network_policies_enabled"):
            recommendations.append("Enable network policies for enhanced security")

        return {
            "status": "FAILED" if issues else "PASSED",
            "issues": issues,
            "recommendations": recommendations
        }
        
def validate_resource_allocation(self, project, config):
    """Validate resource allocation and limits"""
    issues = []
    recommendations = []
    for function in project.functions:
        # Check resource limits
        if not function.spec.resources.get("limits"):
            issues.append(f"No resource limits set for {function.metadata.name}")
        
        # Check requests
        if not function.spec.resources.get("requests"):
            recommendations.append(f"Consider setting resource requests for {function}")
        
        # Check auto-scaling configuration
        if function.kind == "serving" and not hasattr(function.spec, 'min_replicas'):
            recommendations.append(f"Configure auto-scaling for serving function {function}")

    return {
        "status": "FAILED" if issues else "PASSED",
        "issues": issues,
        "recommendations": recommendations
    }


def validate_monitoring_setup(self, project, config):
    """Validate monitoring and observability setup"""
    issues = []
    recommendations = []

    # Check if monitoring is enabled
    if not config.get("monitoring_enabled"):
        issues.append("Model monitoring not enabled")

    # Check alert configuration
    if not project.get_alerts():
        recommendations.append("Configure alerts for production monitoring")

    # Check logging configuration
    if not config.get("structured_logging"):
        recommendations.append("Enable structured logging for better observability")

    return {
        "status": "FAILED" if issues else "PASSED",
        "issues": issues,
        "recommendations": recommendations
    }
    
# Use the validator before production deployment
validator = EnterpriseDeploymentValidator()
validation_result = validator.validate_deployment(project, {
"rbac_enabled": True,
"network_policies_enabled": True,
"monitoring_enabled": True,
"structured_logging": True
})
if validation_result["overall_status"] == "FAILED":
    print("❌ Deployment validation failed:")
    for issue in validation_result["critical_issues"]:
        print(f - {issue})
else:
    print("✅ Deployment validation passed")

# MLRunTroubleshooter class for deployment diagnostics
class MLRunTroubleshooter:
    """Comprehensive troubleshooting for MLRun deployments"""

    def __init__(self):
        self.diagnostic_checks = {
            "function_build_failures": self.diagnose_build_issues,
            "memory_issues": self.diagnose_memory_problems,
            "performance_degradation": self.diagnose_performance_issues,
            "feature_store_latency": self.diagnose_feature_store_issues,
            "model_serving_errors": self.diagnose_serving_issues
        }

    def run_diagnostics(self, project):
        """Run comprehensive diagnostics on MLRun project"""
        diagnostic_results = {}
        for check_name, check_func in self.diagnostic_checks.items():
            try:
                result = check_func(project)
                diagnostic_results[check_name] = result
            except Exception as e:
                diagnostic_results[check_name] = {
                    "status": "ERROR",
                    "message": f"Diagnostic check failed: {str(e)}"
                }
        return diagnostic_results

    def diagnose_build_issues(self, project):
        """Diagnose function build failures"""
        issues = []
        solutions = []
        for function in project.functions:
            try:
                # Check build logs
                if function.status.build_pod:
                    build_logs = function.get_build_logs()
                    if "ERROR" in build_logs or "FAILED" in build_logs:
                        issues.append(f"Build failed for {function.metadata.name}")
                    # Common build issue patterns
                    if "requirements" in build_logs.lower():
                        solutions.append("Check requirements.txt format and package versions")
                    if "base image" in build_logs.lower():
                        solutions.append("Verify base image accessibility and compatibility")
                    if "memory" in build_logs.lower():
                        solutions.append("Increase build memory limits")
            except Exception as e:  
                issues.append(f"Could not check build status for {function.metadata.name}")
        return {
            "status": "ISSUES_FOUND" if issues else "OK",
            "issues": issues,
            "solutions": solutions
        }
        
def diagnose_memory_problems(self, project):
    """Diagnose memory-related issues"""
    issues = []  
    solutions = []
    for function in project.functions:
        # Check resource utilization
        metrics = self.get_function_metrics(function)
        if metrics:
            memory_usage = metrics.get("memory_usage_percent", 0)
            if memory_usage > 90:
                issues.append(f"High memory usage in {function.metadata.name}: {memor")
                solutions.append(f"Increase memory limits for {function.name}")
        # Check for OOM kills
        if metrics.get("oom_kills", 0) > 0:
            issues.append(f"OOM kills detected in {function.metadata.name}")
            solutions.append("Optimize data processing or increase memory allocat")
    return {
        "status": "ISSUES_FOUND" if issues else "OK",
        "issues": issues,
        "solutions": solutions
    }

def optimize_performance(self, project):
    """Apply performance optimizations"""
    optimizations = []
    for function in project.functions:
        # CPU optimization
        if function.kind == "job":
            function.set_env("OMP_NUM_THREADS", "4")
            function.set_env("MKL_NUM_THREADS", "4")
            optimizations.append(f"Applied CPU threading optimization to {function.name}")
        # Memory optimization
        if function.kind == "serving":
            function.spec.build.commands.append("pip install --no-cache-dir ...")
            optimizations.append(f"Enabled no-cache pip installation for {function.name}")
        # Storage optimization
        function.with_annotation("storage.class", "gp3")
        optimizations.append(f"Configured high-performance storage for {function.meta}")
    return {
        "optimizations_applied": len(optimizations),
        "details": optimizations
    }

# Run comprehensive troubleshooting
troubleshooter = MLRunTroubleshooter()
diagnostic_results = troubleshooter.run_diagnostics(project)
# Apply performance optimizations
optimization_results = troubleshooter.optimize_performance(project)
print("Diagnostic Results:", diagnostic_results)
print("Performance Optimizations:", optimization_results)