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Python SDKREST API ↗Tutorials
  • INTRODUCTION
  • Welcome
  • Quick start
    • For Model creators
    • For Data consumers
    • For Fine-tuning LLMs
    • What is synthetic data?
    • Privacy protection
    • Context processing in multi-table datasets
  • Best practices
  • ASSISTANT
  • Assistant
    • Prompt examples
    • Manage threads
    • Python session status
    • Configuration
  • TRAINING & GENERATION
  • 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
    • _RARE_ values
  • Synthetic datasets
    • Generate a new synthetic dataset
    • Set sample size and temperature
    • Rebalance columns
    • Data imputation
    • Evaluate synthetic data quality
    • Deliver synthetic data
    • Seeded generation
    • Fair synthetic data
    • Manage synthetic datasets
  • Connectors
    • Create a connector
      • MySQL
      • PostgreSQL
      • SQL Server
      • Oracle
      • MariaDB
      • Apache Hive
      • BigQuery
      • Snowflake
      • Databricks
      • S3 storage
      • Microsoft Azure blob storage
      • Google Cloud object storage
      • Use as a data source
      • Use as a data destination
      • Use a local database
    • Manage connectors
  • Demo datasets
  • GLOBAL FEATURES
  • Organizations
    • Create an organization
    • Edit organization settings
    • Manage resources
    • Manage members
    • Delete an organization
    • Roles and permissions
  • Public and private resources
  • Search
  • Usage and credits
  • Notifications
  • INSTALLATION AND ADMINISTRATION
    • Architecture
    • Requirements
    • Hardware profiling
    • Configure
      • Compute resources
      • External PostgreSQL database
      • Internal image repository
      • Domain SSL 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
    • Troubleshoot
      • Common issues
      • AWS EKS
      • OpenShift
      • Azure AKS
      • minikube
    • Compliance
    • Compute
    • Models
    • Users and groups
    • 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