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Deployment Architecture Overview

ZenML provides a flexible deployment architecture that supports both local development and production-scale deployments. Understanding the deployment options helps you choose the right approach for your MLOps infrastructure.

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Architecture Components

A ZenML deployment consists of several key components:

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ZenML Server

The ZenML server is the central component that provides:
  • REST API: Exposes endpoints for pipeline management, artifact tracking, and metadata storage
  • Dashboard: Web-based UI for monitoring pipelines and managing resources
  • Authentication: Supports multiple auth schemes (NO_AUTH, HTTP_BASIC, OAUTH2_PASSWORD_BEARER, EXTERNAL)
  • Database Backend: Stores pipeline metadata, artifacts, and configuration (SQLite or MySQL-compatible databases)
  • Secrets Management: Secure storage for credentials and sensitive data

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Database Layer

ZenML supports two database types: SQLite (Development)
  • Local file-based database
  • No external dependencies
  • Suitable for development and testing
  • Limited scalability and no concurrent access
MySQL/MariaDB (Production)
  • External database service
  • High availability and scalability
  • Connection pooling and SSL support
  • Backup and recovery capabilities

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Artifact Store

Stores pipeline artifacts and outputs:
  • Local filesystem (development)
  • Cloud object storage (S3, GCS, Azure Blob)
  • Network-attached storage (NFS, EFS)

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Orchestrator

Executes pipeline steps:
  • Local orchestrator (development)
  • Kubernetes (production)
  • Cloud-managed services (Vertex AI, SageMaker, AzureML)

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Deployment Types

ZenML supports multiple deployment scenarios:

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Local Development

Local Server

Quick setup for development and testing on your local machine.
Characteristics:
  • SQLite database
  • Local artifact storage
  • Single-user access
  • Quick iteration and debugging
Use Cases:
  • Pipeline development
  • Local testing
  • Proof of concepts
  • Learning ZenML

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Docker Deployment

Docker Container

Containerized server deployment for consistent environments.
Characteristics:
  • Isolated environment
  • Reproducible setup
  • Easy to start/stop
  • Configurable resources
Use Cases:
  • Development teams
  • CI/CD integration
  • Local production testing
  • Multi-user development

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Kubernetes Deployment

Kubernetes with Helm

Production-grade deployment with high availability and scalability.
Characteristics:
  • Horizontal scaling
  • High availability
  • Load balancing
  • Advanced networking
  • Resource management
Use Cases:
  • Production deployments
  • Enterprise environments
  • Multi-tenant setups
  • Cloud-native infrastructure

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Cloud-Managed Services

Cloud Platforms

Deploy on managed cloud platforms for fully managed infrastructure.
Characteristics:
  • Fully managed infrastructure
  • Automatic scaling
  • Built-in monitoring
  • Managed databases and storage
Use Cases:
  • AWS (ECS, EKS, App Runner)
  • GCP (Cloud Run, GKE)
  • Azure (Container Apps, AKS)

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Deployment Decision Matrix

Choose your deployment type based on these criteria:
CriteriaLocalDockerKubernetesCloud-Managed
Ease of Setup⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐
Scalability⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐
High Availability❌❌⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐
Multi-User⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐
Production Ready❌⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐
CostFreeLowMediumMedium-High
MaintenanceNoneLowMedium-HighLow

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Authentication Schemes

ZenML server supports multiple authentication methods:

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NO_AUTH

No authentication required. Suitable only for local development. 
zenml connect --url http://localhost:8080 --no-auth

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HTTP_BASIC

Simple username/password authentication. 
zenml connect --url http://localhost:8080 \
  --username admin \
  --password securepass

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OAUTH2_PASSWORD_BEARER

OAuth2-based authentication with JWT tokens (recommended for production). 
zenml connect --url https://zenml.example.com

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EXTERNAL

Integrate with external authentication providers (SSO, LDAP, etc.).

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Environment Variables

Key environment variables for ZenML server configuration:

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Server Configuration

# Enable server mode
ZENML_SERVER=true

# Set deployment type for telemetry
ZENML_SERVER_DEPLOYMENT_TYPE=kubernetes

# Authentication scheme
ZENML_SERVER_AUTH_SCHEME=OAUTH2_PASSWORD_BEARER

# JWT secret key (generate with: openssl rand -hex 32)
ZENML_AUTH_JWT_SECRET_KEY=your-secret-key

# Server URL
ZENML_SERVER_URL=https://zenml.example.com

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Database Configuration

# MySQL database URL
ZENML_STORE_URL=mysql://user:password@host:3306/zenml

# Connection pool settings
ZENML_STORE_POOL_SIZE=20
ZENML_STORE_MAX_OVERFLOW=20

# SSL configuration
ZENML_STORE_SSL_CA=/path/to/ca.crt
ZENML_STORE_SSL_VERIFY_SERVER_CERT=true

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Performance Settings

# Thread pool size for request handling
ZENML_SERVER_THREAD_POOL_SIZE=40

# Authentication thread pool size
ZENML_SERVER_AUTH_THREAD_POOL_SIZE=5

# Request timeout (seconds)
ZENML_SERVER_REQUEST_TIMEOUT=20

# Request cache timeout (seconds)
ZENML_SERVER_REQUEST_CACHE_TIMEOUT=300

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Secrets Store Configuration

# Use SQL secrets store
ZENML_SECRETS_STORE_TYPE=sql
ZENML_SECRETS_STORE_ENCRYPTION_KEY=your-encryption-key

# Or use cloud secrets managers
ZENML_SECRETS_STORE_TYPE=aws
ZENML_SECRETS_STORE_AWS_REGION=us-east-1

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Security Considerations

Network Security

  • Use HTTPS in production
  • Configure proper firewall rules
  • Implement network policies in Kubernetes
  • Use VPC/subnet isolation

Authentication

  • Always use authentication in production
  • Rotate JWT secret keys regularly
  • Use strong passwords
  • Enable external auth for SSO

Secrets Management

  • Encrypt secrets at rest
  • Use cloud secrets managers
  • Rotate credentials regularly
  • Audit secret access

Database Security

  • Use SSL for database connections
  • Verify server certificates
  • Restrict database access
  • Regular backups and encryption

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High Availability Setup

For production deployments requiring high availability:

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Multiple Replicas

# values.yaml for Helm chart
zenml:
  replicaCount: 3
  
autoscaling:
  enabled: true
  minReplicas: 2
  maxReplicas: 10
  targetCPUUtilizationPercentage: 80

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External Database

Use managed database services:
  • AWS RDS (MySQL)
  • Google Cloud SQL (MySQL)
  • Azure Database for MySQL

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Load Balancing

Distribute traffic across replicas:
  • Kubernetes Service (ClusterIP)
  • Ingress Controller (nginx, traefik)
  • Cloud Load Balancers

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Health Checks

Configure readiness and liveness probes: 
livenessProbe:
  httpGet:
    path: /health
    port: http
  initialDelaySeconds: 15
  periodSeconds: 15

readinessProbe:
  httpGet:
    path: /ready
    port: http
  initialDelaySeconds: 8
  periodSeconds: 15

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Monitoring and Observability

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Metrics

ZenML server exposes metrics for monitoring:
  • Request count and latency
  • Database connection pool usage
  • Authentication failures
  • Pipeline run statistics

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Logging

Configure logging verbosity: 
# Set log level
ZENML_LOGGING_VERBOSITY=INFO  # DEBUG, INFO, WARNING, ERROR

# Enable debug mode
ZENML_DEBUG=true

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Health Endpoints

  • /health - Liveness check
  • /ready - Readiness check
  • /version - Server version information

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Next Steps

Server Deployment

Learn about different server deployment options

Kubernetes Deployment

Deploy ZenML on Kubernetes with Helm

Docker Deployment

Run ZenML server in Docker containers

Configuration Guide

Customize your deployment configuration

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References

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