AI Model Deployment with MLOps Architecture

Introduction to MLOps Deployment Architecture

This architecture outlines a production-grade AI model deployment pipeline implementing MLOps best practices. It integrates Model Development (Jupyter/Colab), Experiment Tracking (MLflow), Model Registry for version control, CI/CD Pipelines (GitHub Actions), Containerization (Docker), Orchestration (Kubernetes), and Monitoring (Prometheus/Grafana). The system enables reproducible model packaging, automated canary deployments, A/B testing, drift detection, and rollback capabilities. Security is enforced through signed model artifacts, encrypted storage, and RBAC across all components.

High-Level System Diagram

The workflow begins with Data Scientists developing models in notebooks, logging experiments to MLflow Tracking Server. Validated models are registered in the Model Registry, triggering CI/CD pipelines that build Docker images pushed to a Container Registry. The Kubernetes Operator deploys models as microservices with traffic splitting. Prometheus collects metrics while Evidently monitors data drift. Arrows indicate flows: blue (solid) for development, orange for CI/CD, green for deployment, and purple for monitoring.

Key Components

• Development Environment: JupyterLab/VSCode with experiment tracking
• Version Control: Git repositories for code and model definitions
• Experiment Tracking: MLflow/Weights & Biases for metrics logging
• Model Registry: Centralized storage with stage transitions
• CI/CD Engine: GitHub Actions/Jenkins for automation
• Containerization: Docker with ML-specific base images
• Orchestration: Kubernetes with KFServing/Kubeflow
• Model Serving: FastAPI/TRTIS inference servers
• Monitoring: Prometheus/Grafana for system metrics
• Data Quality: Evidently/WhyLogs for drift detection
• Feature Store: Feast/Tecton for consistent features
• Security: OPA/Gatekeeper for policy enforcement

Benefits of the Architecture

• Reproducibility: Docker + MLflow ensures consistent environments
• Scalability: Kubernetes autoscales inference endpoints
• Governance: Model registry tracks lineage and approvals
• Resilience: Automated rollback on failure detection
• Efficiency: CI/CD eliminates manual deployment steps
• Observability: End-to-end performance tracking

Implementation Considerations

• MLflow Setup: Configure S3-backed artifact storage
• Docker Optimization: Multi-stage builds to reduce image size
• K8s Configuration: Resource limits/requests for predictable performance
• Canary Deployment: Istio traffic splitting for safe rollouts
• Monitoring: Custom metrics for model-specific KPIs
• Security: Pod security policies and network policies
• Cost Control: Cluster autoscaling with spot instances
• Documentation: Model cards for compliance

Example Configuration: MLflow with S3 Backend

# mlflow_server.sh
export MLFLOW_S3_ENDPOINT_URL=https://minio.example.com
export AWS_ACCESS_KEY_ID=your-access-key
export AWS_SECRET_ACCESS_KEY=your-secret-key

mlflow server \
    --backend-store-uri postgresql://mlflow:password@postgres/mlflow \
    --default-artifact-root s3://mlflow-artifacts \
    --host 0.0.0.0

# Dockerfile for model serving
FROM python:3.9-slim
RUN pip install mlflow==2.3.0 boto3 psycopg2-binary

COPY ./model /app
WORKDIR /app

ENTRYPOINT ["mlflow", "models", "serve", \
            "--model-uri", "models:/prod-model/1", \
            "--port", "5000", \
            "--host", "0.0.0.0"]
                

Example Kubernetes Deployment

# deployment.yaml
apiVersion: serving.kubeflow.org/v1beta1
kind: InferenceService
metadata:
  name: fraud-detection
spec:
  predictor:
    containers:
    - name: kfserving-container
      image: registry.example.com/fraud-model:v1.2.0
      ports:
      - containerPort: 8080
      resources:
        limits:
          nvidia.com/gpu: 1
      env:
      - name: MODEL_THRESHOLD
        value: "0.85"
  traffic:
    canary:
      percent: 10
    default:
      percent: 90

# monitoring-service.yaml
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: model-monitor
spec:
  endpoints:
  - port: web
    interval: 30s
    path: /metrics
  selector:
    matchLabels:
      app: fraud-detection