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  • Welcome
  • INTRODUCTION
    • What Is Aizen?
    • Aizen Platform Interfaces
    • Typical ML Workflow
    • Datasets and Features
    • Resources and GPUs
    • LLM Operations
    • Glossary
  • INSTALLATION
    • Setting Up Your Environment
      • Hardware Requirements
      • Deploying Kubernetes On Prem
      • Deploying Kubernetes on AWS
      • Deploying Kubernetes on GCP
        • GCP and S3 API Interoperability
        • Provisioning the Cloud Service Mesh
        • Installing Ingress Gateways with Istio
      • Deploying Kubernetes on Azure
        • Setting Up Azure Blob Storage
    • Installing Aizen
      • Software Requirements
      • Installing the Infrastructure Components
      • Installing the Core Components
      • Virtual Services and Gateways Command Script (GCP)
      • Helpful Deployment Commands
    • Installing Aizen Remote Components
      • Static Remote Deployment
      • Dynamic Remote Deployment
    • Installing Optional Components
      • MinIO
      • OpenLDAP
      • OpenEBS Operator
      • NGINX Ingress Controller
      • Airbyte
  • GETTING STARTED
    • Managing Users and Roles
      • Aizen Security
      • Adding Users
      • Updating Users
      • Listing Users and Roles
      • Granting or Revoking Roles
      • Deleting Users
    • Accessing the Aizen Platform
    • Using the Aizen Jupyter Console
  • MANAGING ML WORKFLOWS
    • ML Workflow
    • Configuring Data Sources
    • Configuring Data Sinks
    • Creating Training Datasets
    • Performing ML Data Analysis
    • Training an ML Model
    • Adding Real-Time Data Sources
    • Serving an ML Model
    • Training and Serving Custom ML Models
  • MANAGING LLM WORKFLOWS
    • LLM Workflow
    • Configuring Data Sources
    • Creating Training Datasets for LLMs
    • Fine-Tuning an LLM
    • Serving an LLM
    • Adding Cloud Providers
    • Configuring Vector Stores
    • Running AI Agents
  • Notebook Commands Reference
    • Notebook Commands
  • SYSTEM CONFIGURATION COMMANDS
    • License Commands
      • check license
      • install license
    • Authorization Commands
      • add users
      • alter users
      • list users
      • grant role
      • list roles
      • revoke role
      • delete users
    • Cloud Provider Commands
      • add cloudprovider
      • list cloudproviders
      • list filesystems
      • list instancetypes
      • status instance
      • list instance
      • list instances
      • delete cloudprovider
    • Project Commands
      • create project
      • alter project
      • exportconfig project
      • importconfig project
      • list projects
      • show project
      • set project
      • listconfig all
      • status all
      • stop all
      • delete project
      • shutdown aizen
    • File Commands
      • install credentials
      • list credentials
      • delete credentials
      • install preprocessor
  • MODEL BUILDING COMMANDS
    • Data Source Commands
      • configure datasource
      • describe datasource
      • listconfig datasources
      • delete datasource
    • Data Sink Commands
      • configure datasink
      • describe datasink
      • listconfig datasinks
      • alter datasink
      • start datasink
      • status datasink
      • stop datasink
      • list datasinks
      • display datasink
      • delete datasink
    • Dataset Commands
      • configure dataset
      • describe dataset
      • listconfig datasets
      • exportconfig dataset
      • importconfig dataset
      • start dataset
      • status dataset
      • stop dataset
      • list datasets
      • display dataset
      • export dataset
      • import dataset
      • delete dataset
    • Data Analysis Commands
      • loader
      • show stats
      • show datatypes
      • show data
      • show unique
      • count rows
      • count missingvalues
      • plot
      • run analysis
      • run pca
      • filter dataframe
      • list dataframes
      • set dataframe
      • save dataframe
    • Training Commands
      • configure training
      • describe training
      • listconfig trainings
      • start training
      • status training
      • list trainings
      • list tensorboard
      • start tensorboard
      • stop tensorboard
      • stop training
      • restart training
      • delete training
      • list mlflow
      • save embedding
      • list trained-models
      • list trained-model
      • export trained-model
      • import trained-model
      • delete trained-model
      • register model
      • update model
      • list registered-models
      • list registered-model
  • MODEL SERVING COMMANDS
    • Resource Commands
      • configure resource
      • describe resource
      • listconfig resources
      • alter resource
      • delete resource
    • Prediction Commands
      • configure prediction
      • describe prediction
      • listconfig predictions
      • start prediction
      • status prediction
      • test prediction
      • list predictions
      • stop prediction
      • list prediction-logs
      • display prediction-log
      • delete prediction
    • Data Report Commands
      • configure datareport
      • describe datareport
      • listconfig datareports
      • start datareport
      • list data-quality
      • list data-drift
      • list target-drift
      • status data-quality
      • display data-quality
      • status data-drift
      • display data-drift
      • status target-drift
      • display target-drift
      • delete datareport
    • Runtime Commands
      • configure runtime
      • describe runtime
      • listconfig runtimes
      • start runtime
      • status runtime
      • stop runtime
      • delete runtime
  • LLM AND EMBEDDINGS COMMANDS
    • LLM Commands
      • configure llm
      • listconfig llms
      • describe llm
      • start llm
      • status llm
      • stop llm
      • delete llm
    • Vector Store Commands
      • configure vectorstore
      • describe vectorstore
      • listconfig vectorstores
      • start vectorstore
      • status vectorstore
      • stop vectorstore
      • delete vectorstore
    • LLM Application Commands
      • configure llmapp
      • describe llmapp
      • listconfig llmapps
      • start llmapp
      • status llmapp
      • stop llmapp
      • delete llmapp
  • TROUBLESHOOTING
    • Installation Issues
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On this page
  • Syntax
  • Parameters
  • Example
  1. MODEL BUILDING COMMANDS
  2. Dataset Commands

configure dataset

The configure dataset command configures a dataset and defines its features. In the notebook, you will be guided through a template form that will prompt you for various inputs, such as the name of the data source, each feature name, and type and definition parameters, such as the source data sink for that feature.

Syntax

configure dataset

Parameters

  1. To create a new dataset, select New from the Dataset drop-down list, and specify a name for the dataset.

    Or:

    To change an existing dataset, select the name of the dataset from the Dataset drop-down list.

  2. To create a new feature, select Create New from the Feature drop-down list, and specify a name for the feature.

    Or:

    To change an existing feature, select the name of the feature from the Feature drop-down list.

  3. Select the feature type.

  4. Specify a name for the feature and optionally a description.

  5. For basis features:

    1. Select the data sink from the drop-down list.

    2. Select the required feature.

  6. For contextual features:

    1. If the feature will use an expression, select the Expression checkbox.

      This checkbox is only enabled if you installed an Aizen license that allows expressions.

      • If you want to use one of Aizen's built-in expressions, select them from the Built-in Expressions drop-down menu.

      • If you want to specify your own expression, leave the Built-in Expressions field blank, or select None and specify the required expression in the expression field.

    2. For aggregate features:

      1. Make sure to clear the Expression checkbox.

      2. Select the input Datasink.

      3. Specify the source column from the data sink.

      4. Select the aggregate function.

      5. Select the timestamp column from the drop-down list.

      6. As needed, specify the appropriate missing strategy to handle missing data for the input feature.

      7. Specify the window start and end values to compute the windowed aggregate values.

      8. Specify the required join key and the feature to map to that key.

  7. Select if the input feature will be treated as a label or output feature.

  8. Select the Materialize checkbox if the feature needs to be materialized and stored in the dataset. If you leave the materialize checkbox cleared, then the feature is only created in memory during dataset creation and used in the computation of other features.

  9. Click on Add Feature to add the feature to the dataset configuration.

  10. Repeat the previous steps to define all the dataset features.

  11. Click the Save Configuration button to start saving the dataset configuration.

  12. Click OK to persist the configuration.

Example

PreviousDataset CommandsNextdescribe dataset

Last updated 3 months ago