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Synthetic Data SDKREST API ↗Tutorials
  • INTRODUCTION
  • Welcome
  • Quick start
    • Datasets
    • Connectors
    • Generators
    • Synthetic datasets
    • Context processing in multi-table datasets
    • Privacy protection
    • What are credits?
    • What is data for everyone?
    • What is mock data?
    • What is synthetic data?
  • Best practices
  • PLATFORM
  • Assistant
    • Artifacts
    • Chats
    • Configuration
    • Prompts
    • Python sessions
  • RESOURCES
  • Datasets
    • Create a dataset
    • Manage datasets
    • Prepare your dataset
    • Public datasets
  • Generators
    • Tabular synthetic data
    • Prepare your data
      • CSV requirements
      • Privacy best practices for original data
    • Train a new generator
      • Add data
      • Set table relationships
        • Two-table scenario
        • Multi-table scenario
        • Manage primary keys
        • Relationship diagram
      • Set encoding types
      • Improve model accuracy
      • Speed up training
      • Configure time-series models
      • Fine-tune privacy mechanisms
      • Enable differential privacy
      • Enable flexible generation
    • Evaluate generator quality
    • Export and import generators
    • Manage generators
    • Live-probe generators
    • Fine-tuning LLMs
    • _RARE_ values
  • Connectors
    • Create a connector
      • Azure Blob Storage
      • Google Cloud Storage
      • AWS S3 Storage
      • Google BigQuery
      • Databricks
      • MariaDB
      • Microsoft SQL Server
      • MySQL
      • Oracle Database
      • PostgreSQL
      • Snowflake
      • Apache Hive
      • Connector access types
      • Use as a data source
      • Use as a data destination
      • Use a local database
    • Manage connectors
  • Synthetic datasets
    • Generate a new synthetic dataset
    • Select a compute
    • Set sample size and temperature
    • Rebalance columns
    • Data imputation
    • Fair synthetic data
    • Conditional simulation
    • Deliver synthetic data
    • Evaluate synthetic data quality
    • Manage synthetic datasets
  • FEATURES
  • Organizations
    • Create an organization
    • Edit organization settings
    • Manage resources
    • Manage members
    • Delete an organization
    • Roles and permissions
  • User profiles
  • Public and private resources
  • Search
  • Usage and credits
  • Notifications
  • ADMINISTRATION
    • Architecture
    • Requirements
    • Hardware profiling
    • Configure
      • Compute resources
      • External PostgreSQL database
      • Internal image repository
      • Domain TLS certificate
      • Ingress controllers
        • HAProxy
        • NGINX
        • Istio Virtual Service
    • Deploy
      • Deployment checklist
      • MOSTLY AI Helm chart
      • Deploy to AWS EKS
      • Deploy to OpenShift
      • Deploy to Google GKE
      • Deploy to Azure AKS
      • Deploy to minikube
      • Deploy via AWS Marketplace
    • Troubleshoot
      • Common issues
      • AWS EKS
      • OpenShift
      • Azure AKS
      • minikube
    • Compliance
    • Computes
    • Models
    • Users
    • Identity providers
    • Storage policy
    • Backup and restore
  • RELEASES AND SUPPORT
  • Release notes
  • Release support lifecycle
  • Support
InstallationConfigureIngress controllers

Ingress controllers

By default, the MOSTLY AI Helm chart supports HAProxy as an ingress controller. Learn how you can enable it for your MOSTLY AI deployment.

Configure HAProxy

Alternatively, you can install and use NGINX.

Install and configure NGINX

MOSTLY AI also provides support for Istio Virtual Service.

Configure Istio Virtual Service