LogoLogo
HomeAcademyLoginTry for free
  • Welcome
  • What's new
    • Q2 2025
    • Q1 2025
    • Q4 2024
    • Q3 2024
    • Q2 2024
    • Q1 2024
    • Q4 2023
    • Q3 2023
    • Q2 2023
    • Q1 2023
    • Q4 2022
    • Q3 2022
  • FAQs
    • Accounts
    • Migration to the new platform
    • User & organization setup
    • General
    • Builder
    • Workflows
    • Data Observatory
    • Analytics Toolbox
    • Development Tools
    • Deployment Options
    • CARTO Basemaps
    • CARTO for Education
    • Support Packages
    • Security and Compliance
  • Getting started
    • What is CARTO?
    • Quickstart guides
      • Connecting to your data
      • Creating your first map
      • Creating your first workflow
      • Developing your first application
    • CARTO Academy
  • CARTO User Manual
    • Overview
      • Creating your CARTO organization
      • CARTO Cloud Regions
      • CARTO Workspace overview
    • Maps
      • Data sources
        • Simple features
        • Spatial Indexes
        • Pre-generated tilesets
        • Rasters
        • Defining source spatial data
        • Managing data freshness
        • Changing data source location
      • Layers
        • Point
          • Grid point aggregation
          • H3 point aggregation
          • Heatmap point aggregation
          • Cluster point aggregation
        • Polygon
        • Line
        • Grid
        • H3
        • Raster
        • Zoom to layer
      • Widgets
        • Formula widget
        • Category widget
        • Pie widget
        • Histogram widget
        • Range widget
        • Time Series widget
        • Table widget
      • SQL Parameters
        • Date parameter
        • Text parameter
        • Numeric parameter
        • Publishing SQL parameters
      • Interactions
      • Legend
      • Basemaps
        • Basemap selector
      • AI Agents
      • SQL analyses
      • Map view modes
      • Map description
      • Feature selection tool
      • Search locations
      • Measure distances
      • Exporting data
      • Download PDF reports
      • Managing maps
      • Publishing and sharing maps
        • Map settings for viewers
        • Map preview for editors
        • Collaborative maps
        • Embedding maps
        • URL parameters
      • Performance considerations
    • Workflows
      • Workflow canvas
      • Results panel
      • Components
        • Aggregation
        • Custom
        • Data Enrichment
        • Data Preparation
        • Generative AI
        • Input / Output
        • Joins
        • Parsers
        • Raster Operations
        • Spatial Accessors
        • Spatial Analysis
        • Spatial Constructors
        • Spatial Indexes
        • Spatial Operations
        • Statistics
        • Tileset Creation
        • BigQuery ML
        • Snowflake ML
        • Google Earth Engine
        • Google Environment APIs
        • Telco Signal Propagation Models
      • Data Sources
      • Scheduling workflows
      • Sharing workflows
      • Using variables in workflows
      • Executing workflows via API
      • Temporary data in Workflows
      • Extension Packages
      • Managing workflows
      • Workflows best practices
    • Data Explorer
      • Creating a map from your data
      • Importing data
        • Importing rasters
      • Geocoding data
      • Optimizing your data
    • Data Observatory
      • Terminology
      • Browsing the Spatial Data Catalog
      • Subscribing to public and premium datasets
      • Accessing free data samples
      • Managing your subscriptions
      • Accessing your subscriptions from your data warehouse
        • Access data in BigQuery
        • Access data in Snowflake
        • Access data in Databricks
        • Access data in Redshift
        • Access data in PostgreSQL
    • Connections
      • Google BigQuery
      • Snowflake
      • Databricks
      • Amazon Redshift
      • PostgreSQL
      • CARTO Data Warehouse
      • Sharing connections
      • Deleting a connection
      • Required permissions
      • IP whitelisting
      • Customer data responsibilities
    • Applications
    • Settings
      • Understanding your organization quotas
      • Activity Data
        • Activity Data Reference
        • Activity Data Examples
        • Activity Data Changelog
      • Users and Groups
        • Inviting users to your organization
        • Managing user roles
        • Deleting users
        • SSO
        • Groups
        • Mapping groups to user roles
      • CARTO Support Access
      • Customizations
        • Customizing appearance and branding
        • Configuring custom color palettes
        • Configuring your organization basemaps
        • Enabling AI Agents
      • Advanced Settings
        • Managing applications
        • Configuring S3 Bucket for Redshift Imports
        • Configuring OAuth connections to Snowflake
        • Configuring OAuth U2M connections to Databricks
        • Configuring S3 Bucket integration for RDS for PostgreSQL Exports in Builder
        • Configuring Workload Identity Federation for BigQuery
      • Data Observatory
      • Deleting your organization
    • Developers
      • Managing Credentials
        • API Base URL
        • API Access Tokens
        • SPA OAuth Clients
        • M2M OAuth Clients
      • Named Sources
  • Data and Analysis
    • Analytics Toolbox Overview
    • Analytics Toolbox for BigQuery
      • Getting access
        • Projects maintained by CARTO in different BigQuery regions
        • Manual installation in your own project
        • Installation in a Google Cloud VPC
        • Core module
      • Key concepts
        • Tilesets
        • Spatial indexes
      • SQL Reference
        • accessors
        • clustering
        • constructors
        • cpg
        • data
        • http_request
        • import
        • geohash
        • h3
        • lds
        • measurements
        • placekey
        • processing
        • quadbin
        • random
        • raster
        • retail
        • routing
        • s2
        • statistics
        • telco
        • tiler
        • transformations
      • Guides
        • Running queries from Builder
        • Working with Raster data
      • Release notes
      • About Analytics Toolbox regions
    • Analytics Toolbox for Snowflake
      • Getting access
        • Native App from Snowflake's Marketplace
        • Manual installation
      • Key concepts
        • Spatial indexes
        • Tilesets
      • SQL Reference
        • accessors
        • clustering
        • constructors
        • data
        • http_request
        • import
        • h3
        • lds
        • measurements
        • placekey
        • processing
        • quadbin
        • random
        • raster
        • retail
        • s2
        • statistics
        • tiler
        • transformations
      • Guides
        • Running queries from Builder
        • Working with Raster data
      • Release Notes
    • Analytics Toolbox for Databricks
      • Getting access
        • Personal (former Single User) cluster
        • Standard (former Shared) cluster
      • Reference
        • lds
        • tiler
      • Guides
      • Release Notes
    • Analytics Toolbox for Redshift
      • Getting access
        • Manual installation in your database
        • Installation in an Amazon Web Services VPC
        • Core version
      • Key concepts
        • Tilesets
        • Spatial indexes
      • SQL Reference
        • clustering
        • constructors
        • data
        • http_request
        • import
        • lds
        • placekey
        • processing
        • quadbin
        • random
        • s2
        • statistics
        • tiler
        • transformations
      • Guides
        • Running queries from Builder
      • Release Notes
    • Analytics Toolbox for PostgreSQL
      • Getting access
        • Manual installation
        • Core version
      • Key concepts
        • Tilesets
        • Spatial Indexes
      • SQL Reference
        • h3
        • quadbin
        • tiler
      • Guides
        • Creating spatial index tilesets
        • Running queries from Builder
      • Release Notes
    • CARTO + Python
      • Installation
      • Authentication Methods
      • Visualizing Data
      • Working with Data
        • How to work with your data in the CARTO Data Warehouse
        • How to access your Data Observatory subscriptions
        • How to access CARTO's Analytics Toolbox for BigQuery and create visualizations via Python notebooks
        • How to access CARTO’s Analytics Toolbox for Snowflake and create visualizations via Python notebooks
        • How to visualize data from Databricks
      • Reference
    • CARTO QGIS Plugin
  • CARTO for Developers
    • Overview
    • Key concepts
      • Architecture
      • Libraries and APIs
      • Authentication methods
        • API Access Tokens
        • OAuth Access Tokens
        • OAuth Clients
      • Connections
      • Data sources
      • Visualization with deck.gl
        • Basemaps
          • CARTO Basemap
          • Google Maps
            • Examples
              • Gallery
              • Getting Started
              • Basic Examples
                • Hello World
                • BigQuery Tileset Layer
                • Data Observatory Tileset Layer
              • Advanced Examples
                • Arc Layer
                • Extrusion
                • Trips Layer
            • What's New
          • Amazon Location
            • Examples
              • Hello World
              • CartoLayer
            • What's New
        • Rapid Map Prototyping
      • Charts and widgets
      • Filtering and interactivity
      • Summary
    • Quickstart
      • Make your first API call
      • Visualize your first dataset
      • Create your first widget
    • Guides
      • Build a public application
      • Build a private application
      • Build a private application using SSO
      • Visualize massive datasets
      • Integrate CARTO in your existing application
      • Use Boundaries in your application
      • Avoid exposing SQL queries with Named Sources
      • Managing cache in your CARTO applications
    • Reference
      • Deck (@deck.gl reference)
      • Data Sources
        • vectorTableSource
        • vectorQuerySource
        • vectorTilesetSource
        • h3TableSource
        • h3QuerySource
        • h3TilesetSource
        • quadbinTableSource
        • quadbinQuerySource
        • quadbinTilesetSource
        • rasterSource
        • boundaryTableSource
        • boundaryQuerySource
      • Layers (@deck.gl/carto)
      • Widgets
        • Data Sources
        • Server-side vs. client-side
        • Models
          • getFormula
          • getCategories
          • getHistogram
          • getRange
          • getScatter
          • getTimeSeries
          • getTable
      • Filters
        • Column filters
        • Spatial filters
      • CARTO APIs Reference
    • Release Notes
    • Examples
    • CARTO for React
      • Guides
        • Getting Started
        • Views
        • Data Sources
        • Layers
        • Widgets
        • Authentication and Authorization
        • Basemaps
        • Look and Feel
        • Query Parameters
        • Code Generator
        • Sample Applications
        • Deployment
        • Upgrade Guide
      • Examples
      • Library Reference
        • Introduction
        • API
        • Auth
        • Basemaps
        • Core
        • Redux
        • UI
        • Widgets
      • Release Notes
  • CARTO Self-Hosted
    • Overview
    • Key concepts
      • Architecture
      • Deployment requirements
    • Quickstarts
      • Single VM deployment (Kots)
      • Orchestrated container deployment (Kots)
      • Advanced Orchestrated container deployment (Helm)
    • Guides
      • Guides (Kots)
        • Configure your own buckets
        • Configure an external in-memory cache
        • Enable Google Basemaps
        • Enable the CARTO Data Warehouse
        • Configure an external proxy
        • Enable BigQuery OAuth connections
        • Configure Single Sign-On (SSO)
        • Use Workload Identity in GCP
        • High availability configuration for CARTO Self-hosted
        • Configure your custom service account
      • Guides (Helm)
        • Configure your own buckets (Helm)
        • Configure an external in-memory cache (Helm)
        • Enable Google Basemaps (Helm)
        • Enable the CARTO Data Warehouse (Helm)
        • Configure an external proxy (Helm)
        • Enable BigQuery OAuth connections (Helm)
        • Configure Single Sign-On (SSO) (Helm)
        • Use Workload Identity in GCP (Helm)
        • Use EKS Pod Identity in AWS (Helm)
        • Enable Redshift imports (Helm)
        • Migrating CARTO Self-hosted installation to an external database (Helm)
        • Advanced customizations (Helm)
        • Configure your custom service account (Helm)
    • Maintenance
      • Maintenance (Kots)
        • Updates
        • Backups
        • Uninstall
        • Rotating keys
        • Monitoring
        • Change the Admin Console password
      • Maintenance (Helm)
        • Monitoring (Helm)
        • Rotating keys (Helm)
        • Uninstall (Helm)
        • Backups (Helm)
        • Updates (Helm)
    • Support
      • Get debug information for Support (Kots)
      • Get debug information for Support (Helm)
    • CARTO Self-hosted Legacy
      • Key concepts
        • Architecture
        • Deployment requirements
      • Quickstarts
        • Single VM deployment (docker-compose)
      • Guides
        • Configure your own buckets
        • Configure an external in-memory cache
        • Enable Google Basemaps
        • Enable the CARTO Data Warehouse
        • Configure an external proxy
        • Enable BigQuery OAuth connections
        • Configure Single Sign-On (SSO)
        • Enable Redshift imports
        • Configure your custom service account
        • Advanced customizations
        • Migrating CARTO Self-Hosted installation to an external database
      • Maintenance
        • Updates
        • Backups
        • Uninstall
        • Rotating keys
        • Monitoring
      • Support
    • Release Notes
  • CARTO Native App for Snowflake Containers
    • Deploying CARTO using Snowflake Container Services
  • Get Help
    • Legal & Compliance
    • Previous libraries and components
    • Migrating your content to the new CARTO platform
Powered by GitBook
On this page
  • Get Model by Name
  • Create Classification Model
  • Create Forecasting Model
  • Predict
  • Forecast
  • Evaluate Classification
  • Evaluate Forecast
  • Feature Importance (Classification)
  • Feature Importance (Forecast)

Was this helpful?

Export as PDF
  1. CARTO User Manual
  2. Workflows
  3. Components

Snowflake ML

Extension Package provided by CARTO

PreviousBigQuery MLNextGoogle Earth Engine

Last updated 1 month ago

Was this helpful?

The Snowflake ML extension package for CARTO Workflows includes a variety of components that enable users to integrate machine learning workflows with geospatial data. These components allow for creating, evaluating, explaining, forecasting, and managing ML models directly within CARTO Workflows, utilizing Snowflake ML’s capabilities.

Get Model by Name

Description

This component imports a pre-trained model into the current workflow. If the name provided is not fully qualified, it will default to the connection's default database and the PUBLIC schema. The component assumes that the provided FQN points to an existing Snowflake ML model.

Settings

  • Model's FQN: Fully qualified name for the model to be imported.

Outputs

  • Output table: This component generates a single-row table with the FQN of the imported model.

Create Classification Model

Description

This component trains a classification model on the provided input data. If the name provided is not fully qualified, it will default to the connection's default database and the PUBLIC schema.

For more details, please refer to the official documentation in Snowflake.

Inputs

  • Input table: A data table that is used as input for the model creation.

Settings

  • Model's FQN: Fully qualified name for the model to be saved as.

  • ID Column: Column containing a unique identifier per sample.

  • Target Column: Column to be used as label in the training data.

  • Data Split: whether to perform or not a train/evaluation split on the data. Choosing SPLIT is required to use the Evaluate component with the resulting model.

  • Test Fraction: Fraction of the data to reserve for evaluation. A 0.2 will reserve 20% of the data for evaluation. Only applies if the Data Split is SPLIT.

Outputs

  • Output table: This component generates a single-row table with the FQN of the imported model.

Create Forecasting Model

Description

This component trains a forecasting model on the provided input data. If the name provided is not fully qualified, it will default to the connection's default database and the PUBLIC schema.

Inputs

  • Input table: A data table that is used as input for the model creation.

Settings

  • Model's FQN: Fully qualified name for the model to be saved as.

  • Time Series ID Column: Column containing a unique identifier per time series.

  • Timestamp Column: Column containing the series' timestamp in DATE or DATETIME format.

  • Target Column: Column to be used as target in the training data.

  • Consider exogenous variables: whether to consider exogenous variables for forecasting. If checked, the future values for the variables must be provided when forecasting. All variables in the input will be considered except the specified time series ID, timestamp and target column.

  • Method: which method to use when fitting the model. It can be best or fast.

  • Sample Frequency: the frequency of the time series. It can be auto or manual.

  • Period: number of units to define the sampling frequency. Only applies when the Sample Frequency has been set to manual.

  • Time Unit: time unit used to define the frequency. It can be seconds, minutes, hours, days, weeks, months, quarters, or years. Only applies when Sampling Frequency has been set to manual.

  • Aggregation (categorical): aggregation function used for categorical columns if needed due to the sampling frequency. It can be mode, first, or last.

  • Aggregation (numeric): aggregation function used for numeric columns if needed due to the sampling frequency. It can be mean, median, mode, min, max, sum, first, or last.

  • Aggregation (target): aggregation function used for the target column if needed due to the sampling frequency. It can be mean, median, mode, min, max, sum, first, or last.

Outputs

  • Output table: This component generates a single-row table with the FQN of the imported model.

Predict

Description

Inputs

  • Model table: the pre-trained classification model.

  • Input table: A data table that is used as input for inference.

Settings

  • Keep input columns: Whether to keep all the input columns in the output table.

  • ID Column: Column containing a unique identifier per sample. Only applies when Keep input columns is set to false.

Outputs

  • Output table: The model's predictions.

Forecast

Description

Inputs

  • Model table: the pre-trained classification model.

  • Input table: A data table that is used as input for inference. Only needed if the model has been trained using exogenous variables.

Settings

  • Consider exogenous variables: whether the model was trained to use exogenous variables or not.

  • Number of periods: number of periods to forecast per time series. Only applies if Consider exogenous variables is false.

  • Time Series ID Column: Column containing a unique identifier per time series. Only applies if Consider exogenous variables is true.

  • Timestamp Column: Column containing the series' timestamp in DATE or DATETIME format. Only applies if Consider exogenous variables is true.

  • Prediction Interval: Expected confidence of the prediction interval.

  • Keep input columns: Whether to keep all the input columns in the output table.

Outputs

  • Output table: The model's predictions.

Evaluate Classification

Description

Inputs

  • Model table: the pre-trained classification model.

Settings

  • Class level metrics: Whether to obtain the per-class evaluation metrics or the overall evaluation metrics.

Outputs

  • Output table: The model's evaluation metrics.

Evaluate Forecast

Description

Inputs

  • Model table: the pre-trained classification model.

  • Input table (optional): additional out-of-sample data to compute the metrics on.

Settings

  • Compute metrics on additional out-of-sample data: When checked, the component will compute cross-validation metrics on additional out-of-sample data. Otherwise, the component will return the metrics generated at training time.

  • Time Series ID Column: Column containing a unique identifier per time series. Only applies when the metrics are being computed on additional out-of-sample data.

  • Timestamp Column: Column containing the series' timestamp in DATE or DATETIME format. Only applies when the metrics are being computed on additional out-of-sample data.

  • Target Column: Column to use as label in the input data. Only applies when the metrics are being computed on additional out-of-sample data.

  • Prediction Interval: Expected confidence of the prediction interval. Only applies when the metrics are being computed on additional out-of-sample data.

Outputs

  • Output table: The model's evaluation metrics.

Feature Importance (Classification)

This component displays the feature importances per variable of a pre-trained classification model.

Inputs

  • Model table: the pre-trained classification model.

Outputs

  • Output table: a table with the feature importance per variable.

Feature Importance (Forecast)

This component displays the feature importances per variable of a pre-trained forecast model.

Inputs

  • Model table: the pre-trained forecast model.

Outputs

  • Output table: a table with the feature importance per variable.

For more details, please refer to the official documentation in Snowflake.

This component uses a pre-trained classification model (using or components) to perform predictions on some given input data.

For more details, please refer to the function official documentation in Snowflake.

This component uses a pre-trained forecast model (using or components) to perform predictions on some given input data.

For more details, please refer to the function official documentation in Snowflake.

This component returns some evaluation metrics for a pre-trained classification model using or components.

For more details, please refer to the and functions official documentation in Snowflake.

This component returns some evaluation metrics for a pre-trained forecast model using or components.

For more details, please refer to the function official documentation in Snowflake.

For more details, please refer to the Snowflake's function documentation.

For more details, please refer to the Snowflake's function documentation.

SNOWFLAKE.ML.CLASSIFICATION
SNOWFLAKE.ML.FORECAST
!PREDICT
!FORECAST
!SHOW_EVALUATION_METRICS
!SHOW_GLOBAL_EVALUATION_METRICS
!SHOW_EVALUATION_METRICS
!SHOW_FEATURE_IMPORTANCE
!SHOW_FEATURE_IMPORTANCE
Get Model by Name
Create Classification Model
Get Model by Name
Create Forecasting Model
Get Model by Name
Create Classification Model
Get Model by Name
Create Forecasting Model