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  • 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
    • Share a generator
    • 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
  • Search
  • Usage and credits
  • Notifications
  • INSTALLATION AND ADMINISTRATION
    • Architecture
    • Requirements
    • Hardware profiling
    • Configure
      • Compute resources
      • External PostgreSQL database
      • Internal image repository
      • Domain SSL certificate
    • 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
Installation
Troubleshoot

Troubleshoot MOSTLY AI deployments

Due to different Kubernetes cluster configurations, you might need troubleshooting information in case you run into any issue deploying MOSTLY AI.

Contact MOSTLY AI if you need help troubleshooting your deployment.

Kubernetes iconTroubleshoot common deployment issuesOpenShift iconTroubleshoot OpenShift cluster deploymentsAmazon EKS iconTroubleshoot AWS EKS cluster deploymentsAzure Managed Kubernetes Services (AKS)Troubleshoot Azure AKS cluster deploymentsMinikube iconTroubleshoot minikube cluster deployments