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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
    • Example artifacts
  • 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
    • Backup and restore
    • Computes
    • Identity providers
    • Models
    • Storage policy
    • Users
  • RELEASES AND SUPPORT
  • Release notes
  • Release support lifecycle
  • Support

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AssistantExample artifacts

Example artifacts

The MOSTLY AI Assistant can generate artifacts that can be saved and shared with other users on MOSTLY AI or other platforms.

You must be logged in to MOSTLY AI in order to view the datasets shared here.

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Dataset: American Community Survey 2023

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Dataset: Apple Music Top-100 Snapshot

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Dataset: Nasa Patents

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Dataset: Nobel Prize Laureates