Magic ETL Tiles: AI Services

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Intro

This article describes how to use the AI Services tiles in Magic ETL. To learn how to use tiles in DataFlows, see Create a Magic ETL DataFlow.

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AI Forecasting Tile

The AI Forecasting tile allows you to predict future data points based on patterns or trends in your data by leveraging Domo’s Forecasting Universal Model. After saving your forecast in an output DataSet, you can view the DataSet in Analyzer to visualize your forecast in a Forecasting Chart. Access the AI Forecasting tile under AI Services in the Magic ETL interface.

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Configure the AI Forecasting Tile

After you connect your input DataSet to an AI Forecasting tile on the Magic ETL canvas, you can configure your model preferences. Select the AI Forecasting tile to expand the tile editor. Complete the steps in the editor. First, choose a model. You can select the Domo ETS Forecasting Univeral Model or the Domo ARIMA Forecasting Universal Model.
Learn more about models. After choosing the model, complete the other steps in the tile editor. This table describes how. After configuring the AI Forecasting tile, finish configuring your Magic ETL DataFlow and create the output Dataset. The output DataSet includes seven columns. This table provides more information about them.

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Configure Your AI Forecasting Tile to Match Its Input Granularity

Drag and drop an AI Forecasting tile onto the authoring canvas and connect to your DataFlow. Select the AI Forecasting tile to expand the tile editor. Select Configuration (tab). Adjust your date and value columns, then choose a forecast frequency and length, and select a seasonality period—all according to your specified granularity.
For example, if you are ordering granularity by month, you can select your configured column “Order Date Month,” a value sum, a “Monthly” frequency, and a seasonality period of “12.”

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Interpret Results from the AI Forecasting Tile

You can interpret and explore the results of your forecast by opening the output DataSet in Analyzer. Use the chart type finder to choose a Forecasting Chart. Use the upper and lower bound columns from your output DataSet to set your bounds and display the forecast confidence ranges. Learn more about the Forecasting Chart.

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Convert Input Granularity

Follow the steps below to convert your input DataSet from a minute granularity to a month/day or other specific granularity when creating a DataFlow with the AI Forecasting tile.

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Add a Granularity Formula

Drag and drop an Add Formula tile onto the authoring canvas. Connect that tile to your DataSet. Select the Add Formula tile to expand the tile editor. Select Configuration (tab). Enter a name in the Name output column field.
For example, to create a column granularity by month, you can enter “Order Date Month.” Enter the corresponding formula in the Write formula field.
For example, to order granularity by month, you can write DATE_TRUNC ('month', 'Order Date').

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Alter Your Column Granularity

Drag and drop an Alter Columns tile onto the authoring canvas. Connect that tile to your DataFlow. Select the Alter Columns tile to expand the tile editor. Select Configuration (tab). Alter applicable columns.
For example, to order granularity by month, you can select the “Order Date Month” column you created with your granularity formula.
Then, change the Cast Data Type to “Date.”

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Group Your Column by Granularity

Drag and drop a Group By tile onto the authoring canvas. Connect that tile to your DataFlow. Select the Group By tile to expand the tile editor. Select Configuration (tab). Select the applicable columns.
For example, to order granularity by month, you can select the “Order Date Month”column you created and altered in the Add Formula and Alter Columns tiles. You now have a column that you have altered and grouped according to your specified granularity.

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Configure Your AI Forecasting Tile to Match Its Input Granularity

Drag and drop an AI Forecasting tile onto the authoring canvas and connect to your DataFlow. Select the AI Forecasting tile to expand the tile editor. Select Configuration (tab). Adjust your date and value columns, then choose a forecast frequency and length, and select a seasonality period—all according to your specified granularity.
For example, if you are ordering granularity by month, you can select your configured column “Order Date Month,” a value sum, a “Monthly” frequency, and a seasonality period of “12.”

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Save Your Granularity Configuration

Select Save to save your configuration. You can also check your work by selecting the dropdown next to Save and selecting Save and Run. The Save and Run DataFlow modal displays. (Optional) Add a version description to explain what you changed. Select Save and Run to run the DataFlow and ensure that all granularity columns in the ETL flow match.

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AI Model Inference Tile

The AI Model Inference tile allows you to gather insights from your input DataSet and use the inferred output DataSet throughout Domo. You can also use the output DataSet from your AI Model Inference tile to create App Studio apps and DataBricks that display data analysis, model performance, model predictions, and what-if analysis examples to share with other people in your organization.

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Required Grant

To access the AI Model Inference tile in Magic ETL, the following grant must be enabled for your role:

• **Use AI Services —**Allows a user to access and use the AI Services in Domo.

For more information on grants, see Managing Custom Roles.

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Configure the AI Model Inference Tile

1. Select the DataSet you want to gather inferences from and open it in Magic ETL.
2. In the Magic ETL sidebar, expand Data Science and drag an AI Model Inference tile to the canvas.
   
3. Connect your input DataSet to the tile. You can only use one DataSet as input.
4. In the tile editor, choose Select a Model to search for and choose a model to use for your inference. The model schema must match the schema of the input DataSet. If the schemas do not match, an error message displays.
   
   If the schema of your input DataSet matches the model’s schema, you can select the model you want to bring in. You can only use models with a CSV type in Magic ETL.
   
5. Depending on the model, you can select a task if multiple options are available in the Pick a Task dropdown. The model task automatically generates from your selected model. Models may have multiple tasks, but only one task is selected for the DataFlow. In the example used above, only the “Other” task type is available.
6. Select a tier, depending on the number of resources used to run the model. A tieris the size of the resources the model is running with and the number of central processing units (CPUs) used. Learn more about compute tiers.
7. (Optional) Check the box labeled Include Input Data in Output Data to duplicate all the rows from your input DataSet. This means that if you have 10 rows of data in your input, there will be 10 rows of data in your output.
   If you leave the box unchecked, only the inferred data is included in the output DataSet.

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Text Generation Tile

Valuable information is often inaccessible inside text columns in your DataSets—and parsing, categorizing, and analyzing text can be manual and time-consuming. The Text Generation tile leverages Domo AI to translate and process text fields, allowing you to analyze and take advantage of your text data’s full value.

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Required Grants

To access the Text Generation tile in Magic ETL, the following grants must be enabled for your role:

• Execute AutoML
• Create AI Service Models
• Use AI Services

Learn more about grants.

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Configure the Text Generation Tile

1. Open the Magic ETL canvas and choose an input DataSet that contains a text column you want to analyze.

2. Under AI Services in the left drawer, drag a Text Generation tile to the canvas. The tile editor expands below the canvas. 3. In the tile editor, choose the AI model you want to use with the Text Generation tile. 4. Choose the Execution Preference.

5. Provide the model with instructions on how to analyze the column. Be very specific; describe exactly what you want the model to do (and not to do).

6. Use the Insert Value dropdown to choose the column that contains the text to be analyzed. You can also provide more details about what should be done with the data. 7. If needed, choose additional Knowledge. 8. Configure the desired output. Choose structured or unstructured output, and either a single column or multiple output columns—each with a name, type, and description. 9. Choose the temperature of your desired response. Lower values provide more predictability, while higher values provide more creativity. After the Text Generation tile executes, it adds a column to your DataSet named Output. You can use tiles farther downstream in the DataFlow to rename that column. Consumption Ramifications:

• AI Operations: 1 operation per tile
• Tokens: input and output tokens vary based on the prompt and the data volume processed

Notes:

• You can include multiple Text Generation tiles in the same DataFlow, tasking them with different aspects of analysis for the column (or analysis of different columns).
• You can give multiple instructions in a single Text Generation tile, although you’ll need a way to parse the results of the multiple instructions from the single output column. For example, you could instruct the model to separate the output of each instruction with a specific delimiter and then use the Split Columns action to parse. Here’s an example of the output DataSet from a DataFlow leveraging multiple instances of the Text Generation tile to analyze language, translate the review to English, analyze sentiment, and categorize the review based on provided themes.
  

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FAQ

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Classification Tile Beta

The Classification tile categorizes data—text, images, or files—into predefined classes. Because the tile comes pre-configured for classification, you can focus on your classification entities instead of configuration details. It’s faster and easier to configure and use than the Text Generation tile for classification.

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Required Grants

To access the Classification tile in Magic ETL, the following grants must be enabled for your role:

• Execute AutoML
• Create AI Service Models
• Use AI Services

Learn more about grants.

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Configure the Classification Tile

1. Open the Magic ETL canvas and choose an input DataSet that contains a text column you want to analyze.

2. Under AI Services in the left drawer, drag a Classification tile to the canvas. The tile editor expands below the canvas. 3. In the tile editor, choose the input data—Data Column or Documents. If the data you want to classify is already in a data column, select the column from the selector. If the data you want to classify is in a Document Collection, select the Document Collection ID and File Path from the selectors.

4. In the tile editor, choose the AI model you want to use with the Classification tile.

5. Choose the Execution Preference.

6. Provide the model with any additional instructions on how to analyze the column. Be very specific; describe exactly what you want the model to do (and not to do).

7. Configure the desired output—Single Label or Multi Label.

• Single Label assigns one class per row. For example, given the classes Corduroy, Denim, and Leather, a row’s Fabric Type resolves to a single value:
  Fabric Type = Denim
  
• Multi Label evaluates each class independently and returns one-hot-encoded Boolean values. For example, given the classes Corduroy, Denim, Leather, Skinny, and Flare:
  Corduroy = True
  Denim = False
  Leather = False
  Skinny = False
  Flare = True
  

After the Classification tile executes, it adds a classification column or columns to your DataSet. You can use tiles farther downstream in the DataFlow to rename that column(s). Consumption Ramifications:

• AI Operations: 1 operation per tile
• Tokens: input and output tokens vary based on the prompt and the data volume processed

Notes:

• You can include multiple Classification tiles in the same DataFlow, tasking them with different aspects of analysis for the column (or analysis of different columns).

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FAQ

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Sentiment Analysis Tile Beta

The Sentiment Analysis tile automatically classifies text—such as reviews, social media posts, and surveys—as positive, negative, or neutral. This helps businesses monitor brand reputation in real time, improve customer service by identifying frustrated users, and analyze trends for better decision-making. Supported values: Positive, Negative, Neutral, Not Applicable Because the tile comes pre-configured for sentiment analysis, you can focus on your topics instead of configuration details. It’s faster and easier to configure and use than the Text Generation tile for sentiment analysis.

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Required Grants

To access the Sentiment Analysis tile in Magic ETL, the following grants must be enabled for your role:

• Execute AutoML
• Create AI Service Models
• Use AI Services

Learn more about grants.

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Configure the Sentiment Analysis Tile

1. Open the Magic ETL canvas and choose an input DataSet that contains a text column you want to analyze.

2. Under AI Services in the left drawer, drag a Sentiment Analysis tile to the canvas. The tile editor expands below the canvas. 3. In the tile editor, choose the input data; Data Column or Documents. If the data you want to analyze is already in a data column, select the column from the selector. If the data you want to classify is in a Document Collection, select the Document Collection ID and File Path from the selectors. 4. In the tile editor, choose the AI model you want to use with the Sentiment Analysis tile. 5. Choose the Execution Preference.

6. Provide the model with any additional instructions on how to analyze the column. Be very specific; describe exactly what you want the model to do (and not to do).

7. Configure the desired output—Overall Sentiment or Targeted Sentiment.

• Overall Sentiment returns a single sentiment value for the text:
  Overall Sentiment = Positive
  
• Targeted Sentiment returns a sentiment value for each topic you specify (for example, overall, fit, durability, and customer service):
  Overall Sentiment = Positive
  Fit = Neutral
  Durability = Positive
  Customer Service = Positive
  

After the Sentiment Analysis tile executes, it adds a sentiment column or columns to your DataSet. You can use tiles farther downstream in the DataFlow to rename that column(s). Consumption Ramifications:

• AI Operations: 1 operation per tile
• Tokens: input and output tokens vary based on the prompt and the data volume processed

Notes:

• You can include multiple Sentiment Analysis tiles in the same DataFlow, tasking them with different aspects of analysis for the column (or analysis of different columns).

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FAQ