Production MLOps Pipeline

End-to-end MLOps infrastructure for financial services firm achieved 99.9% uptime and reduced deployment time from weeks to hours with automated monitoring and CI/CD

99.9%
System Uptime
95%
Faster Deployment
24/7
Automated Monitoring
50+
Models in Production

The Challenge

A leading financial services firm with $100B+ in assets under management was struggling with their machine learning deployment process. Their data science team had developed numerous high-performing models for risk assessment, fraud detection, and algorithmic trading, but deploying these models to production was a manual, error-prone process that took weeks or even months.

The key challenges included:

Solution Architecture

We designed and implemented a comprehensive MLOps pipeline that automated the entire machine learning lifecycle from development to production monitoring.

Data Ingestion
Feature Engineering
Model Training
Model Validation
Deployment
Monitoring

Data Pipeline

  • Apache Kafka for real-time data streaming
  • Apache Airflow for batch processing orchestration
  • Delta Lake for versioned data storage
  • Great Expectations for data quality validation

Model Development

  • MLflow for experiment tracking and model registry
  • Kubeflow Pipelines for automated training
  • Ray for distributed hyperparameter tuning
  • DVC for model and data versioning

Deployment & Serving

  • Kubernetes for container orchestration
  • Seldon Core for model serving
  • Istio for service mesh and traffic management
  • NVIDIA Triton for high-performance inference

Monitoring & Observability

  • Prometheus for metrics collection
  • Grafana for visualization and dashboards
  • Evidently AI for model drift detection
  • Jaeger for distributed tracing
Kubernetes MLflow Apache Airflow Kafka Seldon Core Prometheus Grafana Docker Terraform GitLab CI/CD

Implementation Details

CI/CD Pipeline Configuration

# GitLab CI/CD pipeline for ML model deployment
stages:
  - data-validation
  - model-training
  - model-validation
  - staging-deployment
  - integration-tests
  - production-deployment
  - monitoring-setup

model-training:
  stage: model-training
  image: python:3.9
  script:
    - pip install -r requirements.txt
    - python -m mlflow run . --experiment-name ${CI_COMMIT_REF_NAME}
    - mlflow models build-docker -m "models:/${MODEL_NAME}/${MODEL_VERSION}" -n ${MODEL_NAME}:${MODEL_VERSION}
  artifacts:
    paths:
      - model_artifacts/
    expire_in: 30 days
  only:
    - develop
    - master

staging-deployment:
  stage: staging-deployment
  image: bitnami/kubectl:latest
  script:
    - kubectl apply -f k8s/staging/
    - kubectl set image deployment/${MODEL_NAME}-staging ${MODEL_NAME}=${MODEL_NAME}:${MODEL_VERSION}
    - kubectl rollout status deployment/${MODEL_NAME}-staging
  environment:
    name: staging
    url: https://staging-api.company.com
  only:
    - develop

production-deployment:
  stage: production-deployment
  image: bitnami/kubectl:latest
  script:
    - kubectl apply -f k8s/production/
    - kubectl patch deployment ${MODEL_NAME} -p '{"spec":{"template":{"spec":{"containers":[{"name":"${MODEL_NAME}","image":"${MODEL_NAME}:${MODEL_VERSION}"}]}}}}'
    - kubectl rollout status deployment/${MODEL_NAME}
  environment:
    name: production
    url: https://api.company.com
  when: manual
  only:
    - master

Model Monitoring System

# Automated model monitoring with Evidently AI
import evidently
from evidently import ColumnMapping
from evidently.report import Report
from evidently.metric_preset import DataDriftPreset, TargetDriftPreset
import pandas as pd
import logging

class ModelMonitor:
    def __init__(self, reference_data, model_name):
        self.reference_data = reference_data
        self.model_name = model_name
        self.logger = logging.getLogger(__name__)
        
    def detect_drift(self, current_data, threshold=0.1):
        """Detect data and target drift in production data"""
        
        # Configure column mapping
        column_mapping = ColumnMapping(
            target='target',
            prediction='prediction',
            numerical_features=['feature1', 'feature2', 'feature3'],
            categorical_features=['category1', 'category2']
        )
        
        # Create drift report
        report = Report(metrics=[
            DataDriftPreset(),
            TargetDriftPreset()
        ])
        
        report.run(
            reference_data=self.reference_data,
            current_data=current_data,
            column_mapping=column_mapping
        )
        
        # Extract drift metrics
        drift_results = report.as_dict()
        
        # Check for significant drift
        data_drift_score = drift_results['metrics'][0]['result']['dataset_drift']
        target_drift_score = drift_results['metrics'][1]['result']['drift_score']
        
        if data_drift_score > threshold or target_drift_score > threshold:
            self.trigger_retraining_alert(data_drift_score, target_drift_score)
            
        return {
            'data_drift': data_drift_score,
            'target_drift': target_drift_score,
            'drift_detected': data_drift_score > threshold or target_drift_score > threshold
        }
    
    def trigger_retraining_alert(self, data_drift, target_drift):
        """Trigger alert for model retraining"""
        alert_message = f"""
        Model drift detected for {self.model_name}:
        - Data drift score: {data_drift:.3f}
        - Target drift score: {target_drift:.3f}
        
        Recommend immediate model retraining and validation.
        """
        
        # Send to Slack/PagerDuty/etc.
        self.send_alert(alert_message)
        
        # Log the event
        self.logger.warning(f"Drift detected for {self.model_name}: data={data_drift}, target={target_drift}")
        
    def calculate_model_performance(self, y_true, y_pred, y_proba=None):
        """Calculate comprehensive model performance metrics"""
        from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
        
        metrics = {
            'accuracy': accuracy_score(y_true, y_pred),
            'precision': precision_score(y_true, y_pred, average='weighted'),
            'recall': recall_score(y_true, y_pred, average='weighted'),
            'f1_score': f1_score(y_true, y_pred, average='weighted')
        }
        
        if y_proba is not None:
            metrics['auc_roc'] = roc_auc_score(y_true, y_proba[:, 1])
            
        return metrics

Automated Retraining Pipeline

# Kubeflow pipeline for automated model retraining
from kfp import dsl, components
from kfp.components import create_component_from_func

@create_component_from_func
def data_validation_op(data_path: str, validation_rules_path: str) -> bool:
    """Validate data quality before retraining"""
    import great_expectations as ge
    import pandas as pd
    
    # Load data and validation rules
    df = pd.read_parquet(data_path)
    context = ge.get_context()
    
    # Run validation
    validator = context.get_validator(df)
    results = validator.validate()
    
    return results.success

@create_component_from_func
def model_training_op(data_path: str, model_config: dict) -> str:
    """Train new model version"""
    import mlflow
    import mlflow.sklearn
    from sklearn.ensemble import RandomForestClassifier
    import pandas as pd
    
    # Load training data
    df = pd.read_parquet(data_path)
    X = df.drop('target', axis=1)
    y = df['target']
    
    # Start MLflow run
    with mlflow.start_run():
        # Train model
        model = RandomForestClassifier(**model_config)
        model.fit(X, y)
        
        # Log model
        model_uri = mlflow.sklearn.log_model(model, "model").model_uri
        
    return model_uri

@dsl.pipeline(name='automated-retraining-pipeline')
def retraining_pipeline(
    data_path: str,
    model_config: dict,
    validation_rules_path: str
):
    """Complete automated retraining pipeline"""
    
    # Step 1: Validate data quality
    validation_task = data_validation_op(data_path, validation_rules_path)
    
    # Step 2: Train new model (conditional on validation success)
    with dsl.Condition(validation_task.output == True):
        training_task = model_training_op(data_path, model_config)
        
        # Step 3: Model validation and A/B testing would follow...

Results & Impact

Performance Improvements

Business Value

Technical Achievements

Lessons Learned

Key Insights

Challenges Overcome

Best Practices Established

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