MLOps Maturity: Level 0-3, Manual to Full Automation

MLOps Maturity Overview

The MLOps maturity model provides a framework for organizations to assess and improve their ML operations capabilities.

Maturity Levels Overview

┌─────────────────────────────────────────────────────────────────┐
│                MLOps Maturity Levels                             │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  Level 0: Manual Process                                       │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │ • Manual model training and deployment                  │   │
│  │ • No version control for data/models                    │   │
│  │ • Ad-hoc processes                                     │   │
│  │ • Single data scientist working in silos                │   │
│  └─────────────────────────────────────────────────────────┘   │
│                              │                                  │
│                              ▼                                  │
│  Level 1: ML Pipeline Automation                               │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │ • Automated training pipelines                          │   │
│  │ • Basic experiment tracking                             │   │
│  │ • Model versioning                                     │   │
│  │ • Basic monitoring                                     │   │
│  └─────────────────────────────────────────────────────────┘   │
│                              │                                  │
│                              ▼                                  │
│  Level 2: CI/CD for ML                                        │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │ • Continuous integration for ML code                    │   │
│  │ • Automated testing (data, model, integration)          │   │
│  │ • Automated deployment pipelines                        │   │
│  │ • Feature stores                                       │   │
│  └─────────────────────────────────────────────────────────┘   │
│                              │                                  │
│                              ▼                                  │
│  Level 3: Full Automation                                      │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │ • Automated retraining on drift                         │   │
│  │ • A/B testing and canary deployments                    │   │
│  │ • Full monitoring with auto-remediation                 │   │
│  │ • ML governance and compliance                          │   │
│  └─────────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────────┘

Level 0: Manual Process

Characteristics

At Level 0, ML processes are manual and ad-hoc:

LEVEL_0_CHARACTERISTICS = {
    "training": {
        "process": "Manual script execution",
        "frequency": "On-demand",
        "reproducibility": "Low - manual environment setup",
        "tracking": "Manual notebooks, no versioning"
    },
    "deployment": {
        "process": "Manual model copy to production",
        "rollback": "Manual file restoration",
        "testing": "Ad-hoc validation",
        "monitoring": "None or basic logging"
    },
    "infrastructure": {
        "compute": "Individual workstations",
        "storage": "Local files, shared drives",
        "version_control": "Limited Git usage",
        "environments": "Inconsistent across team"
    },
    "team": {
        "structure": "Data scientists work in silos",
        "collaboration": "Email, meetings",
        "knowledge_sharing": "Minimal"
    }
}

Pain Points at Level 0

LEVEL_0_PAIN_POINTS = [
    "Model training takes days/weeks due to manual processes",
    "No reproducibility - results vary between runs",
    "Deployment is risky and error-prone",
    "Model performance degrades silently in production",
    "Team collaboration is inefficient",
    "No audit trail for compliance",
    "Cannot scale to multiple models",
    "Technical debt accumulates rapidly"
]

Transition to Level 1

class Level0ToLevel1Transition:
    """Transition from Level 0 to Level 1."""
    
    def __init__(self):
        self.milestones = [
            "Implement version control for code and data",
            "Set up experiment tracking",
            "Create automated training pipeline",
            "Establish basic model registry",
            "Implement basic monitoring"
        ]
    
    def create_implementation_plan(self) -> dict:
        """Create implementation plan."""
        
        plan = {
            "phase_1_foundation": {
                "duration": "4-6 weeks",
                "tasks": [
                    "Set up Git repositories with proper branching strategy",
                    "Implement DVC for data versioning",
                    "Deploy MLflow for experiment tracking",
                    "Create standard project structure"
                ],
                "deliverables": [
                    "Git repository with CI",
                    "DVC configuration",
                    "MLflow server",
                    "Project template"
                ]
            },
            "phase_2_automation": {
                "duration": "6-8 weeks",
                "tasks": [
                    "Build automated training pipeline",
                    "Implement model versioning",
                    "Create basic monitoring",
                    "Set up model registry"
                ],
                "deliverables": [
                    "Airflow/Kubeflow pipeline",
                    "Model registry",
                    "Basic Prometheus metrics",
                    "Grafana dashboard"
                ]
            },
            "phase_3_optimization": {
                "duration": "4-6 weeks",
                "tasks": [
                    "Optimize pipeline performance",
                    "Implement data validation",
                    "Create deployment automation",
                    "Establish team workflows"
                ],
                "deliverables": [
                    "Optimized pipelines",
                    "Data validation framework",
                    "Deployment scripts",
                    "Team documentation"
                ]
            }
        }
        
        return plan

Level 1: ML Pipeline Automation

Characteristics

At Level 1, basic ML pipeline automation is in place:

LEVEL_1_CHARACTERISTICS = {
    "training": {
        "process": "Automated pipelines with orchestration",
        "frequency": "Scheduled or triggered",
        "reproducibility": "Medium - pipeline defined in code",
        "tracking": "MLflow/W&B for experiments"
    },
    "deployment": {
        "process": "Scripted deployment with basic validation",
        "rollback": "Version-based rollback",
        "testing": "Model performance validation",
        "monitoring": "Basic metrics (latency, errors)"
    },
    "infrastructure": {
        "compute": "Shared compute cluster",
        "storage": "Centralized data lake",
        "version_control": "Git for code and models",
        "environments": "Containerized training"
    },
    "team": {
        "structure": "Cross-functional ML teams",
        "collaboration": "Shared dashboards, regular syncs",
        "knowledge_sharing": "Model cards, documentation"
    }
}

Level 1 Implementation

# level1_pipeline.py
from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime, timedelta
import mlflow
import json

default_args = {
    'owner': 'ml-team',
    'depends_on_past': False,
    'email_on_failure': True,
    'retries': 2,
    'retry_delay': timedelta(minutes=5),
}

class Level1MLPipeline:
    """Level 1 ML Pipeline Implementation."""
    
    def __init__(self, config: dict):
        self.config = config
        mlflow.set_experiment(config['experiment_name'])
    
    def prepare_data(self):
        """Prepare training data."""
        
        # Load data
        import pandas as pd
        df = pd.read_parquet(self.config['data_path'])
        
        # Basic validation
        assert len(df) > 0, "Empty dataset"
        assert 'label' in df.columns, "Missing label column"
        
        # Log dataset info
        mlflow.log_param("dataset_rows", len(df))
        mlflow.log_param("dataset_columns", len(df.columns))
        
        return df
    
    def train_model(self, df):
        """Train model with experiment tracking."""
        
        from sklearn.model_selection import train_test_split
        import xgboost as xgb
        
        with mlflow.start_run():
            # Split data
            X = df.drop(columns=['label'])
            y = df['label']
            X_train, X_val, y_train, y_val = train_test_split(
                X, y, test_size=0.2, random_state=42
            )
            
            # Train model
            params = self.config.get('model_params', {})
            
            dtrain = xgb.DMatrix(X_train, label=y_train)
            dval = xgb.DMatrix(X_val, label=y_val)
            
            model = xgb.train(
                params,
                dtrain,
                num_boost_round=100,
                evals=[(dval, 'val')],
                early_stopping_rounds=10
            )
            
            # Evaluate
            from sklearn.metrics import roc_auc_score
            val_pred = model.predict(dval)
            auc = roc_auc_score(y_val, val_pred)
            
            # Log metrics and params
            mlflow.log_params(params)
            mlflow.log_metric("auc_roc", auc)
            
            # Save model
            mlflow.xgboost.log_model(model, "model")
            
            return {'model': model, 'auc': auc}
    
    def evaluate_model(self, model, X_val, y_val):
        """Evaluate model and check quality gates."""
        
        from sklearn.metrics import (
            roc_auc_score, precision_score, recall_score
        )
        
        y_pred = model.predict(X_val)
        
        metrics = {
            'auc_roc': roc_auc_score(y_val, y_pred),
            'precision': precision_score(y_val, (y_pred > 0.5).astype(int)),
            'recall': recall_score(y_val, (y_pred > 0.5).astype(int))
        }
        
        # Check quality gates
        thresholds = self.config.get('thresholds', {})
        
        for metric, threshold in thresholds.items():
            if metric in metrics and metrics[metric] < threshold:
                raise ValueError(
                    f"Quality gate failed: {metric}={metrics[metric]} < {threshold}"
                )
        
        return metrics

def create_level1_dag():
    """Create Level 1 Airflow DAG."""
    
    config = {
        'experiment_name': 'level1_ml_experiment',
        'data_path': 's3://ml-data/training/',
        'model_params': {
            'objective': 'binary:logistic',
            'max_depth': 6,
            'learning_rate': 0.1
        },
        'thresholds': {
            'auc_roc': 0.85
        }
    }
    
    pipeline = Level1MLPipeline(config)
    
    with DAG(
        'level1_ml_pipeline',
        default_args=default_args,
        schedule_interval='@daily',
        start_date=datetime(2024, 1, 1),
        catchup=False
    ) as dag:
        
        prepare = PythonOperator(
            task_id='prepare_data',
            python_callable=pipeline.prepare_data
        )
        
        train = PythonOperator(
            task_id='train_model',
            python_callable=pipeline.train_model
        )
        
        evaluate = PythonOperator(
            task_id='evaluate_model',
            python_callable=pipeline.evaluate_model
        )
        
        prepare >> train >> evaluate
    
    return dag

Level 2: CI/CD for ML

Characteristics

At Level 2, CI/CD practices are applied to ML:

LEVEL_2_CHARACTERISTICS = {
    "training": {
        "process": "Automated with data/model validation",
        "frequency": "Event-driven or scheduled",
        "reproducibility": "High - full pipeline versioning",
        "tracking": "Comprehensive experiment tracking"
    },
    "deployment": {
        "process": "CI/CD with automated testing",
        "rollback": "Automated rollback on failure",
        "testing": "Data, model, and integration tests",
        "monitoring": "Comprehensive with drift detection"
    },
    "infrastructure": {
        "compute": "Kubernetes with auto-scaling",
        "storage": "Feature store, model registry",
        "version_control": "GitOps for all components",
        "environments": "Consistent dev/staging/prod"
    },
    "team": {
        "structure": "Platform team + product teams",
        "collaboration": "Shared tools and practices",
        "knowledge_sharing": "Automated documentation"
    }
}

Level 2 CI/CD Pipeline

# .github/workflows/ml-cicd.yaml
name: ML CI/CD Pipeline

on:
  push:
    branches: [main]
    paths:
      - 'src/**'
      - 'models/**'
      - 'config/**'
  pull_request:
    branches: [main]

jobs:
  # Code quality checks
  code-quality:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v3
    
    - name: Set up Python
      uses: actions/setup-python@v4
      with:
        python-version: '3.9'
    
    - name: Install dependencies
      run: |
        pip install -r requirements.txt
        pip install black flake8 mypy
    
    - name: Format check
      run: black --check src/
    
    - name: Lint
      run: flake8 src/
    
    - name: Type check
      run: mypy src/

  # Data validation
  data-validation:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v3
    
    - name: Validate training data
      run: |
        python -m src.data.validate \
          --input s3://ml-data/training/latest/ \
          --config config/data_schema.yaml

  # Model training
  training:
    needs: [code-quality, data-validation]
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v3
    
    - name: Train model
      run: |
        python -m src.models.train \
          --config config/train_config.yaml \
          --experiment ${{ github.sha }}
    
    - name: Upload model artifact
      uses: actions/upload-artifact@v3
      with:
        name: model
        path: models/

  # Model evaluation
  evaluation:
    needs: training
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v3
    
    - name: Download model
      uses: actions/download-artifact@v3
      with:
        name: model
    
    - name: Evaluate model
      run: |
        python -m src.models.evaluate \
          --model models/model.pkl \
          --test-data s3://ml-data/test/ \
          --thresholds config/thresholds.yaml
    
    - name: Generate model card
      run: |
        python -m src documentation.generate_card \
          --model models/model.pkl \
          --output model_card.md

  # Model deployment (staging)
  deploy-staging:
    needs: evaluation
    runs-on: ubuntu-latest
    if: github.ref == 'refs/heads/main'
    steps:
    - uses: actions/checkout@v3
    
    - name: Deploy to staging
      run: |
        kubectl apply -f k8s/staging/ \
          --set image.tag=${{ github.sha }}
    
    - name: Run integration tests
      run: |
        python -m src.tests.integration \
          --endpoint http://staging.ml.example.com

  # Model deployment (production)
  deploy-production:
    needs: deploy-staging
    runs-on: ubuntu-latest
    if: github.ref == 'refs/heads/main'
    environment: production
    steps:
    - uses: actions/checkout@v3
    
    - name: Deploy to production
      run: |
        kubectl apply -f k8s/production/ \
          --set image.tag=${{ github.sha }}
    
    - name: Verify deployment
      run: |
        python -m src.monitoring.verify \
          --endpoint http://ml.example.com

  # Monitoring setup
  monitoring:
    needs: deploy-production
    runs-on: ubuntu-latest
    steps:
    - name: Update monitoring dashboards
      run: |
        python -m src.monitoring.update_dashboards \
          --model-name fraud_detection \
          --version ${{ github.sha }}
    
    - name: Configure alerts
      run: |
        python -m src.monitoring.configure_alerts \
          --model-name fraud_detection \
          --config config/alerts.yaml

Level 2 Testing Framework

# tests/ml_tests.py
import pytest
import numpy as np
import pandas as pd
from typing import Dict

class TestDataValidation:
    """Data validation tests."""
    
    def test_schema_validation(self):
        """Test data schema matches expected."""
        import yaml
        
        with open('config/data_schema.yaml') as f:
            expected_schema = yaml.safe_load(f)
        
        df = pd.read_parquet('s3://ml-data/training/latest/')
        
        # Check columns
        expected_columns = set(expected_schema['columns'])
        actual_columns = set(df.columns)
        
        assert expected_columns == actual_columns
    
    def test_data_quality(self):
        """Test data quality checks."""
        df = pd.read_parquet('s3://ml-data/training/latest/')
        
        # Check missing values
        missing_rates = df.isna().mean()
        assert (missing_rates < 0.1).all()
        
        # Check row count
        assert len(df) > 1000
    
    def test_feature_distributions(self):
        """Test feature distributions are within expected ranges."""
        df = pd.read_parquet('s3://ml-data/training/latest/')
        
        for col in df.select_dtypes(include=['int64', 'float64']).columns:
            # Check for extreme skewness
            from scipy.stats import skew
            skewness = abs(skew(df[col].dropna()))
            assert skewness < 10, f"Feature {col} has extreme skewness"

class TestModelPerformance:
    """Model performance tests."""
    
    @pytest.fixture
    def trained_model(self):
        """Load trained model."""
        import joblib
        return joblib.load('models/model.pkl')
    
    @pytest.fixture
    def test_data(self):
        """Load test data."""
        return pd.read_parquet('s3://ml-data/test/')
    
    def test_model_accuracy(self, trained_model, test_data):
        """Test model meets minimum accuracy threshold."""
        from sklearn.metrics import roc_auc_score
        
        X = test_data.drop(columns=['label'])
        y = test_data['label']
        
        y_pred = trained_model.predict(X)
        auc = roc_auc_score(y, y_pred)
        
        assert auc >= 0.85, f"Model AUC {auc} below threshold"
    
    def test_prediction_distribution(self, trained_model, test_data):
        """Test prediction distribution is reasonable."""
        X = test_data.drop(columns=['label'])
        
        predictions = trained_model.predict(X)
        
        # Check predictions are in valid range
        assert (predictions >= 0).all() and (predictions <= 1).all()
        
        # Check prediction variance
        assert np.std(predictions) > 0.01
    
    def test_model_fairness(self, trained_model, test_data):
        """Test model fairness across groups."""
        X = test_data.drop(columns=['label'])
        y = test_data['label']
        
        # Assuming 'gender' column exists
        if 'gender' in test_data.columns:
            for group in test_data['gender'].unique():
                mask = test_data['gender'] == group
                group_auc = roc_auc_score(y[mask], trained_model.predict(X[mask]))
                
                # Check group AUC is within acceptable range
                assert group_auc >= 0.80, f"Group {group} AUC {group_auc} too low"

class TestIntegration:
    """Integration tests for ML pipeline."""
    
    def test_pipeline_end_to_end(self):
        """Test complete pipeline runs successfully."""
        from src.pipeline import MLPipeline
        
        pipeline = MLPipeline(config_path='config/pipeline.yaml')
        
        # Run pipeline
        results = pipeline.run()
        
        assert results['status'] == 'success'
        assert results['metrics']['auc_roc'] >= 0.85
    
    def test_model_serving(self):
        """Test model serving endpoint."""
        import requests
        
        response = requests.post(
            'http://localhost:8080/predict',
            json={
                'features': {
                    'feature_1': 1.0,
                    'feature_2': 2.0,
                    'feature_3': 3.0
                }
            }
        )
        
        assert response.status_code == 200
        
        result = response.json()
        assert 'prediction' in result
        assert 0 <= result['prediction'] <= 1
    
    def test_monitoring_endpoints(self):
        """Test monitoring endpoints are available."""
        import requests
        
        # Health check
        response = requests.get('http://localhost:8080/health')
        assert response.status_code == 200
        
        # Metrics
        response = requests.get('http://localhost:9090/metrics')
        assert response.status_code == 200

Level 3: Full Automation

Characteristics

At Level 3, ML processes are fully automated with self-healing capabilities:

LEVEL_3_CHARACTERISTICS = {
    "training": {
        "process": "Automated retraining on drift/performance",
        "frequency": "Continuous or event-driven",
        "reproducibility": "Complete lineage tracking",
        "tracking": "Full audit trail with governance"
    },
    "deployment": {
        "process": "Canary/A-B with automated rollback",
        "rollback": "Instant automated rollback",
        "testing": "Comprehensive test suite",
        "monitoring": "Predictive monitoring with auto-remediation"
    },
    "infrastructure": {
        "compute": "Multi-cloud with cost optimization",
        "storage": "Distributed feature store",
        "version_control": "GitOps with policy enforcement",
        "environments": "Ephemeral environments"
    },
    "team": {
        "structure": "ML platform + embedded ML engineers",
        "collaboration": "Self-service tools and documentation",
        "knowledge_sharing": "Automated knowledge base"
    }
}

Level 3 Self-Healing System

import numpy as np
from typing import Dict, List, Optional
from datetime import datetime, timedelta
import json

class SelfHealingMLSystem:
    """Level 3 self-healing ML system."""
    
    def __init__(self, config: Dict):
        self.config = config
        self.monitoring = MonitoringSystem(config)
        self.retraining = AutomatedRetraining(config)
        self.deployment = CanaryDeployment(config)
    
    def run_continuous_optimization(self):
        """Run continuous optimization loop."""
        
        while True:
            # Monitor system health
            health_status = self.monitoring.check_health()
            
            if health_status['status'] == 'degraded':
                # Trigger self-healing
                self._handle_degradation(health_status)
            
            # Check for drift
            drift_status = self.monitoring.check_drift()
            
            if drift_status['drift_detected']:
                # Trigger retraining
                self.retraining.trigger_retraining(
                    reason='data_drift',
                    drift_score=drift_status['drift_score']
                )
            
            # Optimize resources
            self._optimize_resources()
            
            # Wait before next check
            time.sleep(self.config.get('check_interval', 300))
    
    def _handle_degradation(self, health_status: Dict):
        """Handle system degradation."""
        
        issues = health_status.get('issues', [])
        
        for issue in issues:
            if issue['type'] == 'latency_spike':
                self._handle_latency_spike(issue)
            elif issue['type'] == 'accuracy_drop':
                self._handle_accuracy_drop(issue)
            elif issue['type'] == 'error_rate':
                self._handle_error_rate(issue)
    
    def _handle_latency_spike(self, issue: Dict):
        """Handle latency spike."""
        
        # Check if we need to scale up
        current_replicas = self.deployment.get_current_replicas()
        
        if issue['severity'] == 'high':
            # Scale up immediately
            new_replicas = min(
                current_replicas * 2,
                self.config.get('max_replicas', 20)
            )
            self.deployment.scale_deployment(new_replicas)
        
        elif issue['severity'] == 'medium':
            # Scale up gradually
            new_replicas = min(
                current_replicas + 2,
                self.config.get('max_replicas', 20)
            )
            self.deployment.scale_deployment(new_replicas)
    
    def _handle_accuracy_drop(self, issue: Dict):
        """Handle accuracy drop."""
        
        # Check if retraining is needed
        if issue['accuracy_drop'] > 0.1:
            # Trigger emergency retraining
            self.retraining.trigger_retraining(
                reason='accuracy_emergency',
                priority='high'
            )
        else:
            # Schedule retraining
            self.retraining.schedule_retraining(
                reason='accuracy_decay',
                priority='medium'
            )
    
    def _handle_error_rate(self, issue: Dict):
        """Handle high error rate."""
        
        if issue['error_rate'] > 0.1:
            # Rollback to previous stable version
            self.deployment.rollback_to_stable()
        
        elif issue['error_rate'] > 0.05:
            # Scale up and monitor
            current_replicas = self.deployment.get_current_replicas()
            self.deployment.scale_deployment(current_replicas + 2)
    
    def _optimize_resources(self):
        """Optimize resource utilization."""
        
        # Get current utilization
        utilization = self.monitoring.get_resource_utilization()
        
        # Optimize GPU allocation
        if utilization['gpu'] < 0.3:
            # Scale down GPU resources
            self.deployment.optimize_gpu_allocation(reduce=True)
        elif utilization['gpu'] > 0.8:
            # Scale up GPU resources
            self.deployment.optimize_gpu_allocation(reduce=False)
        
        # Optimize memory
        if utilization['memory'] > 0.9:
            # Alert or scale up
            self.monitoring.send_alert(
                'memory_high',
                f"Memory utilization at {utilization['memory']*100:.1f}%"
            )

class MonitoringSystem:
    """Comprehensive monitoring system."""
    
    def __init__(self, config: Dict):
        self.config = config
    
    def check_health(self) -> Dict:
        """Check overall system health."""
        
        issues = []
        
        # Check latency
        latency_stats = self._get_latency_stats()
        if latency_stats['p99'] > self.config.get('latency_threshold', 1.0):
            issues.append({
                'type': 'latency_spike',
                'severity': 'high',
                'value': latency_stats['p99']
            })
        
        # Check error rate
        error_rate = self._get_error_rate()
        if error_rate > self.config.get('error_threshold', 0.05):
            issues.append({
                'type': 'error_rate',
                'severity': 'high',
                'value': error_rate
            })
        
        # Check accuracy
        accuracy = self._get_current_accuracy()
        baseline_accuracy = self.config.get('baseline_accuracy', 0.9)
        if baseline_accuracy - accuracy > 0.05:
            issues.append({
                'type': 'accuracy_drop',
                'severity': 'medium',
                'accuracy_drop': baseline_accuracy - accuracy
            })
        
        status = 'healthy' if not issues else 'degraded'
        
        return {
            'status': status,
            'issues': issues,
            'timestamp': datetime.now().isoformat()
        }
    
    def check_drift(self) -> Dict:
        """Check for data drift."""
        
        from scipy.stats import ks_2samp
        
        # Get reference and current data
        reference_data = self._get_reference_data()
        current_data = self._get_current_data()
        
        drift_detected = False
        drift_scores = {}
        
        for col in reference_data.columns:
            if reference_data[col].dtype in ['int64', 'float64']:
                stat, p_value = ks_2samp(
                    reference_data[col].dropna(),
                    current_data[col].dropna()
                )
                
                drift_scores[col] = stat
                
                if p_value < 0.05:
                    drift_detected = True
        
        return {
            'drift_detected': drift_detected,
            'drift_scores': drift_scores,
            'timestamp': datetime.now().isoformat()
        }
    
    def get_resource_utilization(self) -> Dict:
        """Get resource utilization metrics."""
        
        return {
            'cpu': 0.65,
            'memory': 0.75,
            'gpu': 0.45,
            'timestamp': datetime.now().isoformat()
        }
    
    def send_alert(self, alert_type: str, message: str):
        """Send alert through configured channels."""
        
        print(f"ALERT [{alert_type}]: {message}")
    
    def _get_latency_stats(self) -> Dict:
        """Get latency statistics."""
        return {'p99': 0.5, 'p95': 0.3, 'mean': 0.1}
    
    def _get_error_rate(self) -> float:
        """Get current error rate."""
        return 0.02
    
    def _get_current_accuracy(self) -> float:
        """Get current model accuracy."""
        return 0.92
    
    def _get_reference_data(self):
        """Get reference data for drift detection."""
        return pd.DataFrame({'feature': np.random.randn(1000)})
    
    def _get_current_data(self):
        """Get current data for drift detection."""
        return pd.DataFrame({'feature': np.random.randn(1000) + 0.1})

class AutomatedRetraining:
    """Automated retraining system."""
    
    def __init__(self, config: Dict):
        self.config = config
        self.retraining_queue = []
    
    def trigger_retraining(self, reason: str, 
                          priority: str = 'medium',
                          drift_score: float = None):
        """Trigger model retraining."""
        
        job = {
            'job_id': f"retrain_{datetime.now():%Y%m%d%H%M%S}",
            'reason': reason,
            'priority': priority,
            'drift_score': drift_score,
            'triggered_at': datetime.now().isoformat(),
            'status': 'queued'
        }
        
        self.retraining_queue.append(job)
        
        # Execute if high priority
        if priority == 'high':
            self._execute_retraining(job)
        
        return job
    
    def schedule_retraining(self, reason: str,
                           priority: str = 'low',
                           schedule_time: datetime = None):
        """Schedule retraining for later."""
        
        if schedule_time is None:
            schedule_time = datetime.now() + timedelta(hours=1)
        
        job = {
            'job_id': f"retrain_{datetime.now():%Y%m%d%H%M%S}",
            'reason': reason,
            'priority': priority,
            'scheduled_time': schedule_time.isoformat(),
            'status': 'scheduled'
        }
        
        self.retraining_queue.append(job)
        
        return job
    
    def _execute_retraining(self, job: Dict):
        """Execute retraining job."""
        
        print(f"Executing retraining job: {job['job_id']}")
        
        # Run training pipeline
        # This would call your actual training pipeline
        job['status'] = 'completed'
        job['completed_at'] = datetime.now().isoformat()

class CanaryDeployment:
    """Canary deployment system."""
    
    def __init__(self, config: Dict):
        self.config = config
    
    def get_current_replicas(self) -> int:
        """Get current replica count."""
        return 3
    
    def scale_deployment(self, replicas: int):
        """Scale deployment to specified replicas."""
        
        print(f"Scaling deployment to {replicas} replicas")
    
    def rollback_to_stable(self):
        """Rollback to stable version."""
        
        print("Rolling back to stable version")
    
    def optimize_gpu_allocation(self, reduce: bool = True):
        """Optimize GPU allocation."""
        
        if reduce:
            print("Reducing GPU allocation")
        else:
            print("Increasing GPU allocation")
    
    def canary_deploy(self, new_version: str,
                     traffic_percentage: int = 10):
        """Deploy canary version."""
        
        print(f"Deploying canary {new_version} with {traffic_percentage}% traffic")

Maturity Assessment

Assessment Framework

from typing import Dict, List
from dataclasses import dataclass

@dataclass
class MaturityDimension:
    name: str
    current_level: int
    target_level: int
    gaps: List[str]
    recommendations: List[str]

class MLOpsMaturityAssessment:
    """Assess MLOps maturity across dimensions."""
    
    def __init__(self):
        self.dimensions = self._define_dimensions()
    
    def _define_dimensions(self) -> Dict[str, Dict]:
        """Define maturity dimensions and criteria."""
        
        return {
            "version_control": {
                "level_0": ["No version control", "Manual file management"],
                "level_1": ["Git for code", "Basic branching"],
                "level_2": ["Git for code and data", "DVC/LFS", "Branch protection"],
                "level_3": ["GitOps", "Policy enforcement", "Automated compliance"]
            },
            "experiment_tracking": {
                "level_0": ["Notebooks", "Manual logging"],
                "level_1": ["MLflow/W&B", "Basic parameter tracking"],
                "level_2": ["Comprehensive tracking", "Model registry"],
                "level_3": ["Full lineage", "Automated documentation"]
            },
            "pipeline_automation": {
                "level_0": ["Manual scripts", "Ad-hoc execution"],
                "level_1": ["Airflow/Kubeflow", "Scheduled runs"],
                "level_2": ["Event-driven", "CI/CD integration"],
                "level_3": ["Self-healing", "Auto-optimization"]
            },
            "testing": {
                "level_0": ["Manual validation", "Ad-hoc testing"],
                "level_1": ["Basic unit tests", "Model validation"],
                "level_2": ["Data validation", "Integration tests"],
                "level_3": ["Comprehensive test suite", "Chaos engineering"]
            },
            "monitoring": {
                "level_0": ["No monitoring", "Manual checks"],
                "level_1": ["Basic metrics", "Grafana dashboards"],
                "level_2": ["Drift detection", "Alerting"],
                "level_3": ["Predictive monitoring", "Auto-remediation"]
            },
            "deployment": {
                "level_0": ["Manual deployment", "File copy"],
                "level_1": ["Scripted deployment", "Basic validation"],
                "level_2": ["CI/CD", "Canary deployments"],
                "level_3": ["Automated A/B testing", "Self-healing"]
            }
        }
    
    def assess_dimension(self, dimension: str,
                        current_state: List[str]) -> MaturityDimension:
        """Assess maturity for a single dimension."""
        
        criteria = self.dimensions[dimension]
        
        # Determine current level
        current_level = 0
        for level in range(3, 0, -1):
            level_criteria = criteria[f'level_{level}']
            if any(criterion in current_state for criterion in level_criteria):
                current_level = level
                break
        
        # Determine gaps
        gaps = []
        for level in range(current_level + 1, 4):
            level_criteria = criteria[f'level_{level}']
            gaps.extend(level_criteria)
        
        # Generate recommendations
        recommendations = self._generate_recommendations(
            dimension, current_level, gaps
        )
        
        return MaturityDimension(
            name=dimension,
            current_level=current_level,
            target_level=min(current_level + 1, 3),
            gaps=gaps,
            recommendations=recommendations
        )
    
    def _generate_recommendations(self, dimension: str,
                                 current_level: int,
                                 gaps: List[str]) -> List[str]:
        """Generate recommendations for improvement."""
        
        recommendations = {
            "version_control": {
                0: ["Implement Git for all code", "Create branching strategy"],
                1: ["Add DVC for data versioning", "Implement branch protection"],
                2: ["Adopt GitOps practices", "Implement policy enforcement"]
            },
            "experiment_tracking": {
                0: ["Deploy MLflow/W&B", "Establish tracking standards"],
                1: ["Implement model registry", "Add comprehensive metrics"],
                2: ["Enable full lineage tracking", "Automate documentation"]
            },
            "pipeline_automation": {
                0: ["Deploy Airflow/Kubeflow", "Create standard pipelines"],
                1: ["Implement event-driven triggers", "Add CI/CD integration"],
                2: ["Implement self-healing", "Add auto-optimization"]
            }
        }
        
        return recommendations.get(dimension, {}).get(current_level, [])
    
    def generate_assessment_report(self, 
                                  assessments: List[MaturityDimension]) -> Dict:
        """Generate comprehensive assessment report."""
        
        overall_score = sum(a.current_level for a in assessments) / len(assessments)
        
        return {
            'timestamp': datetime.now().isoformat(),
            'overall_maturity_score': overall_score,
            'overall_level': int(overall_score),
            'dimensions': [
                {
                    'name': a.name,
                    'current_level': a.current_level,
                    'target_level': a.target_level,
                    'gaps': a.gaps,
                    'recommendations': a.recommendations
                }
                for a in assessments
            ],
            'priority_areas': [
                a.name for a in assessments
                if a.current_level < 2
            ],
            'roadmap': self._generate_roadmap(assessments)
        }
    
    def _generate_roadmap(self, 
                         assessments: List[MaturityDimension]) -> Dict:
        """Generate implementation roadmap."""
        
        roadmap = {
            'short_term': [],  # 0-3 months
            'medium_term': [],  # 3-6 months
            'long_term': []  # 6-12 months
        }
        
        for assessment in assessments:
            if assessment.current_level == 0:
                roadmap['short_term'].extend(assessment.recommendations[:2])
            elif assessment.current_level == 1:
                roadmap['medium_term'].extend(assessment.recommendations[:2])
            elif assessment.current_level == 2:
                roadmap['long_term'].extend(assessment.recommendations[:2])
        
        return roadmap

Summary

The MLOps maturity model provides a roadmap for organizational transformation:

1. Level 0: Manual processes - focus on establishing basic practices
2. Level 1: Pipeline automation - implement automated training and tracking
3. Level 2: CI/CD for ML - add automated testing and deployment
4. Level 3: Full automation - implement self-healing and optimization

Assess your current maturity, identify gaps, and create a roadmap for improvement. Maturity is a journey, not a destination.